From e5e241242f1945dc0b8ce9b5fd4134a7b24aafc5 Mon Sep 17 00:00:00 2001 From: "Yuan (Terry) Tang" Date: Sun, 24 Jan 2016 10:41:00 -0500 Subject: [PATCH 01/77] Added skflow link under tensorflow section --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 1249022..e4a2f4a 100644 --- a/README.md +++ b/README.md @@ -294,6 +294,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - TensorFlow - [Website](http://tensorflow.org/) - [TensorFlow Examples for Beginners](https://github.com/aymericdamien/TensorFlow-Examples) + - [Simplified Scikit-learn Style Interface to TensorFlow](https://github.com/tensorflow/skflow) - [Learning TensorFlow GitHub Repo](https://github.com/chetannaik/learning_tensorflow) - [Benchmark TensorFlow GitHub](https://github.com/soumith/convnet-benchmarks/issues/66) From b53dc2f1dc9ef46f6ab478c95f621ae63719450d Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Thu, 4 Feb 2016 22:46:25 +0100 Subject: [PATCH 02/77] update readme with deep learning ressources victor schmidt + tim dettmers + chris olah + michael nielsen --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index e4a2f4a..be0a8c5 100644 --- a/README.md +++ b/README.md @@ -253,6 +253,10 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Artificial Neural Networks Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks) - [Neural Networks FAQs on Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50) - [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html) +- [Deep Learning Implementation Tutorials - Kearas and Lasagne](http://github.com/vict0rsch/deep_learning/) +- [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/) +- [Chris Olah's blog on Neural Networks](https://colah.github.io/) +- [Awesome introduction to Neural Networks, how they learn and how they are coded by M. Nielsen](http://neuralnetworksanddeeplearning.com/) - Neural Machine Translation - [Introduction to Neural Machine Translation with GPUs (part 1)](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) From e3257eb3cec8bd7ad9bca14caa161ae04c400d0b Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Thu, 4 Feb 2016 22:48:42 +0100 Subject: [PATCH 03/77] typo keras --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index be0a8c5..952030b 100644 --- a/README.md +++ b/README.md @@ -253,7 +253,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Artificial Neural Networks Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks) - [Neural Networks FAQs on Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50) - [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html) -- [Deep Learning Implementation Tutorials - Kearas and Lasagne](http://github.com/vict0rsch/deep_learning/) +- [Deep Learning Implementation Tutorials - Keras and Lasagne](http://github.com/vict0rsch/deep_learning/) - [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/) - [Chris Olah's blog on Neural Networks](https://colah.github.io/) - [Awesome introduction to Neural Networks, how they learn and how they are coded by M. Nielsen](http://neuralnetworksanddeeplearning.com/) From 8c98b64bf748287da954f6f2233e88aeba8157ab Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Sun, 14 Feb 2016 17:53:11 +0530 Subject: [PATCH 04/77] Update README.md --- README.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 952030b..f926ec7 100644 --- a/README.md +++ b/README.md @@ -120,6 +120,7 @@ If you want to contribute to this list, please read [Contributing Guidelines](ht - [r4stats](http://r4stats.com/) - analyze the world of data science, and to help people learn to use R - [Variance Explained](http://varianceexplained.org/) - David Robinson's Blog - [AI Junkie](http://www.ai-junkie.com/) - a blog about Artificial Intellingence +- [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/)- Making deep learning accessible ##Resources on Quora @@ -253,10 +254,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Artificial Neural Networks Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks) - [Neural Networks FAQs on Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50) - [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html) -- [Deep Learning Implementation Tutorials - Keras and Lasagne](http://github.com/vict0rsch/deep_learning/) -- [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/) -- [Chris Olah's blog on Neural Networks](https://colah.github.io/) -- [Awesome introduction to Neural Networks, how they learn and how they are coded by M. Nielsen](http://neuralnetworksanddeeplearning.com/) +- [Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/) - Neural Machine Translation - [Introduction to Neural Machine Translation with GPUs (part 1)](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) @@ -281,6 +279,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [RBM using Theano](http://deeplearning.net/tutorial/rbm.html#rbm) - [DBNs using Theano](http://deeplearning.net/tutorial/DBN.html#dbn) - [All Codes](https://github.com/lisa-lab/DeepLearningTutorials) + - [Deep Learning Implementation Tutorials - Keras and Lasagne](http://github.com/vict0rsch/deep_learning/) - [Torch](http://torch.ch/) - [Torch ML Tutorial](http://code.madbits.com/wiki/doku.php), [Code](https://github.com/torch/tutorials) From 2d4ef61c9e22a969e20a9a34bd0ba9995c6f99c4 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Wed, 18 May 2016 21:45:57 +0800 Subject: [PATCH 05/77] Update README.md --- README.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f926ec7..ebdc60c 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,13 @@ # Machine Learning Tutorials [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) -This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this [list](https://github.com/sindresorhus/awesome). +- This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this [list](https://github.com/sindresorhus/awesome). + +- If you want to contribute to this list, please read [Contributing Guidelines](https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/contributing.md). + +- [Curated list of R tutorials for Data Science, NLP and Machine Learning](https://github.com/ujjwalkarn/DataScienceR). + +- [Curated list of Python tutorials for Data Science, NLP and Machine Learning](https://github.com/ujjwalkarn/DataSciencePython). -If you want to contribute to this list, please read [Contributing Guidelines](https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/contributing.md). ##Table of Contents - [Miscellaneous](#general) From 006ba1cd856864c00d55787e32a679e25b7882dc Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Wed, 18 May 2016 21:48:58 +0800 Subject: [PATCH 06/77] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ebdc60c..77550f8 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Machine Learning Tutorials [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) +# Machine Learning and Deep Learning Tutorials [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) - This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this [list](https://github.com/sindresorhus/awesome). From 6dbf0510736fd4dd3817a7c9d13a7eb77bcbf1a6 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Wed, 18 May 2016 21:55:18 +0800 Subject: [PATCH 07/77] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 77550f8..f584656 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Machine Learning and Deep Learning Tutorials [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) +# Machine Learning & Deep Learning Tutorials [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) - This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this [list](https://github.com/sindresorhus/awesome). From ed2048adbdedd6e5c06e39dfd0e4f1218d163e13 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Thu, 19 May 2016 00:18:03 +0800 Subject: [PATCH 08/77] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f584656..f539e34 100644 --- a/README.md +++ b/README.md @@ -630,3 +630,4 @@ Learning](http://www.ima.umn.edu/2011-2012/W3.26-30.12/activities/Wright-Steve/s ##Other Tutorials - For a collection of Data Science Tutorials using R, please refer to [this list](https://github.com/ujjwalkarn/DataScienceR). +- For a collection of Data Science Tutorials using Python, please refer to [this list](https://github.com/ujjwalkarn/DataSciencePython). From 563088b49c7e1625a60670ce6ec39e98cf44a95c Mon Sep 17 00:00:00 2001 From: ReadmeCritic Date: Wed, 18 May 2016 20:48:26 -0700 Subject: [PATCH 09/77] Update README URLs based on HTTP redirects --- README.md | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index f539e34..4b52192 100644 --- a/README.md +++ b/README.md @@ -117,7 +117,7 @@ - [Alex Minnaar's Blog](http://alexminnaar.com/) - A blog about Machine Learning and Software Engineering - [Statistically Significant](http://andland.github.io/) - Andrew Landgraf's Data Science Blog - [Simply Statistics](http://simplystatistics.org/) - A blog by three biostatistics professors -- [Yanir Seroussi's Blog](http://yanirseroussi.com/) - A blog about Data Science and beyond +- [Yanir Seroussi's Blog](https://yanirseroussi.com/) - A blog about Data Science and beyond - [fastML](http://fastml.com/) - Machine learning made easy - [Trevor Stephens Blog](http://trevorstephens.com/) - Trevor Stephens Personal Page - [no free hunch | kaggle](http://blog.kaggle.com/) - The Kaggle Blog about all things Data Science @@ -140,7 +140,7 @@ ##Kaggle Competitions WriteUp -- [How to almost win Kaggle Competitions](http://yanirseroussi.com/2014/08/24/how-to-almost-win-kaggle-competitions/) +- [How to almost win Kaggle Competitions](https://yanirseroussi.com/2014/08/24/how-to-almost-win-kaggle-competitions/) - [Convolution Neural Networks for EEG detection](http://blog.kaggle.com/2015/10/05/grasp-and-lift-eeg-detection-winners-interview-3rd-place-team-hedj/) - [Facebook Recruiting III Explained](http://alexminnaar.com/tag/kaggle-competitions.html) - [Predicting CTR with Online ML](http://mlwave.com/predicting-click-through-rates-with-online-machine-learning/) @@ -169,10 +169,10 @@ - [Applying and Interpreting Linear Regression](http://www.dataschool.io/applying-and-interpreting-linear-regression/) - [What does having constant variance in a linear regression model mean?](http://stats.stackexchange.com/questions/52089/what-does-having-constant-variance-in-a-linear-regression-model-mean/52107?stw=2#52107) - [Difference between linear regression on y with x and x with y](http://stats.stackexchange.com/questions/22718/what-is-the-difference-between-linear-regression-on-y-with-x-and-x-with-y?lq=1) - - [Is linear regression valid when the dependant variable is not normally distributed?](http://www.researchgate.net/post/Is_linear_regression_valid_when_the_outcome_dependant_variable_not_normally_distributed) + - [Is linear regression valid when the dependant variable is not normally distributed?](https://www.researchgate.net/post/Is_linear_regression_valid_when_the_outcome_dependant_variable_not_normally_distributed) - Multicollinearity and VIF - [Dummy Variable Trap | Multicollinearity](https://en.wikipedia.org/wiki/Multicollinearity) - - [Dealing with multicollinearity using VIFs](http://jonlefcheck.net/2012/12/28/dealing-with-multicollinearity-using-variance-inflation-factors/) + - [Dealing with multicollinearity using VIFs](https://jonlefcheck.net/2012/12/28/dealing-with-multicollinearity-using-variance-inflation-factors/) - [Residual Analysis](#residuals-) - [Interpreting plot.lm() in R](http://stats.stackexchange.com/questions/58141/interpreting-plot-lm) @@ -235,8 +235,8 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning) - [Lots of Deep Learning Resources](http://deeplearning4j.org/documentation.html) - [Interesting Deep Learning and NLP Projects (Stanford)](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) -- [Core Concepts of Deep Learning](http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) -- [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) +- [Core Concepts of Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) +- [Understanding Natural Language with Deep Neural Networks Using Torch](https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) - [Stanford Deep Learning Tutorial](http://ufldl.stanford.edu/tutorial/) - [Deep Learning FAQs on Quora](https://www.quora.com/topic/Deep-Learning/faq) - [Google+ Deep Learning Page](https://plus.google.com/communities/112866381580457264725) @@ -252,7 +252,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Awesome Deep Learning Reading List](http://deeplearning.net/reading-list/) - [Deep Learning Comprehensive Website](http://deeplearning.net/), [Software](http://deeplearning.net/software_links/) - [deeplearning Tutorials](http://deeplearning4j.org/) -- [AWESOME! Deep Learning Tutorial](http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) +- [AWESOME! Deep Learning Tutorial](https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) - [Deep Learning Basics](http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html) - [Stanford Tutorials](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/) - [Train, Validation & Test in Artificial Neural Networks](http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ) @@ -262,8 +262,8 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/) - Neural Machine Translation - - [Introduction to Neural Machine Translation with GPUs (part 1)](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) - - [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning](http://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/) + - [Introduction to Neural Machine Translation with GPUs (part 1)](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) + - [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/) - Deep Learning Frameworks @@ -284,7 +284,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [RBM using Theano](http://deeplearning.net/tutorial/rbm.html#rbm) - [DBNs using Theano](http://deeplearning.net/tutorial/DBN.html#dbn) - [All Codes](https://github.com/lisa-lab/DeepLearningTutorials) - - [Deep Learning Implementation Tutorials - Keras and Lasagne](http://github.com/vict0rsch/deep_learning/) + - [Deep Learning Implementation Tutorials - Keras and Lasagne](https://github.com/vict0rsch/deep_learning/) - [Torch](http://torch.ch/) - [Torch ML Tutorial](http://code.madbits.com/wiki/doku.php), [Code](https://github.com/torch/tutorials) @@ -297,7 +297,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) - Caffe - - [Deep Learning for Computer Vision with Caffe and cuDNN](http://devblogs.nvidia.com/parallelforall/deep-learning-computer-vision-caffe-cudnn/) + - [Deep Learning for Computer Vision with Caffe and cuDNN](https://devblogs.nvidia.com/parallelforall/deep-learning-computer-vision-caffe-cudnn/) - TensorFlow - [Website](http://tensorflow.org/) @@ -331,7 +331,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [An application of RNN](http://hackaday.com/2015/10/15/73-computer-scientists-created-a-neural-net-and-you-wont-believe-what-happened-next/) - [Optimizing RNN Performance](http://svail.github.io/) - [Simple RNN](http://outlace.com/Simple-Recurrent-Neural-Network/) - - [Auto-Generating Clickbait with RNN](http://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/) + - [Auto-Generating Clickbait with RNN](https://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/) - [Sequence Learning using RNN (Slides)](http://www.slideshare.net/indicods/general-sequence-learning-with-recurrent-neural-networks-for-next-ml) - [Machine Translation using RNN (Paper)](http://emnlp2014.org/papers/pdf/EMNLP2014179.pdf) - [Music generation using RNNs (Keras)](https://github.com/MattVitelli/GRUV) @@ -408,7 +408,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Original LDA Paper](https://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf) - [alpha and beta in LDA](http://datascience.stackexchange.com/questions/199/what-does-the-alpha-and-beta-hyperparameters-contribute-to-in-latent-dirichlet-a) - [Intuitive explanation of the Dirichlet distribution](https://www.quora.com/What-is-an-intuitive-explanation-of-the-Dirichlet-distribution) - - [Topic modeling made just simple enough](http://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/) + - [Topic modeling made just simple enough](https://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/) - [Online LDA](http://alexminnaar.com/online-latent-dirichlet-allocation-the-best-option-for-topic-modeling-with-large-data-sets.html), [Online LDA with Spark](http://alexminnaar.com/distributed-online-latent-dirichlet-allocation-with-apache-spark.html) - [LDA in Scala](http://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-i-the-theory.html), [Part 2](http://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-ii-the-code.html) - [Segmentation of Twitter Timelines via Topic Modeling](http://alexperrier.github.io/jekyll/update/2015/09/16/segmentation_twitter_timelines_lda_vs_lsa.html) @@ -416,7 +416,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - word2vec - - [Google word2vec](https://code.google.com/p/word2vec/) + - [Google word2vec](https://code.google.com/archive/p/word2vec) - [Bag of Words Model Wiki](https://en.wikipedia.org/wiki/Bag-of-words_model) - [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) - [Skip Gram Model Tutorial](http://alexminnaar.com/word2vec-tutorial-part-i-the-skip-gram-model.html), [CBoW Model](http://alexminnaar.com/word2vec-tutorial-part-ii-the-continuous-bag-of-words-model.html) @@ -492,7 +492,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [What is entropy and information gain in the context of building decision trees?](http://stackoverflow.com/questions/1859554/what-is-entropy-and-information-gain) - [Slides Related to Decision Trees](http://www.slideshare.net/pierluca.lanzi/machine-learning-and-data-mining-11-decision-trees) - [How do decision tree learning algorithms deal with missing values?](http://stats.stackexchange.com/questions/96025/how-do-decision-tree-learning-algorithms-deal-with-missing-values-under-the-hoo) -- [Using Surrogates to Improve Datasets with Missing Values](http://www.salford-systems.com/videos/tutorials/tips-and-tricks/using-surrogates-to-improve-datasets-with-missing-values) +- [Using Surrogates to Improve Datasets with Missing Values](https://www.salford-systems.com/videos/tutorials/tips-and-tricks/using-surrogates-to-improve-datasets-with-missing-values) - [Good Article](https://www.mindtools.com/dectree.html) - [Are decision trees almost always binary trees?](http://stats.stackexchange.com/questions/12187/are-decision-trees-almost-always-binary-trees) - [Pruning Decision Trees](https://en.wikipedia.org/wiki/Pruning_(decision_trees)), [Grafting of Decision Trees](https://en.wikipedia.org/wiki/Grafting_(decision_trees)) @@ -530,7 +530,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Measures of variable importance in random forests](http://stats.stackexchange.com/questions/12605/measures-of-variable-importance-in-random-forests) - [Compare R-squared from two different Random Forest models](http://stats.stackexchange.com/questions/13869/compare-r-squared-from-two-different-random-forest-models) - [OOB Estimate Explained | RF vs LDA](https://stat.ethz.ch/education/semesters/ss2012/ams/slides/v10.2.pdf) -- [Evaluating Random Forests for Survival Analysis Using Prediction Error Curve](http://www.jstatsoft.org/article/view/v050i11) +- [Evaluating Random Forests for Survival Analysis Using Prediction Error Curve](https://www.jstatsoft.org/index.php/jss/article/view/v050i11) - [Why doesn't Random Forest handle missing values in predictors?](http://stats.stackexchange.com/questions/98953/why-doesnt-random-forest-handle-missing-values-in-predictors) - [How to build random forests in R with missing (NA) values?](http://stackoverflow.com/questions/8370455/how-to-build-random-forests-in-r-with-missing-na-values) - [FAQs about Random Forest](http://stats.stackexchange.com/questions/tagged/random-forest), [More FAQs](http://stackoverflow.com/questions/tagged/random-forest) @@ -580,7 +580,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. ##Stacking Models - [Stacking, Blending and Stacked Generalization](http://www.chioka.in/stacking-blending-and-stacked-generalization/) - [Stacked Generalization (Stacking)](http://machine-learning.martinsewell.com/ensembles/stacking/) -- [Stacked Generalization: when does it work?](http://www.ijcai.org/Past%20Proceedings/IJCAI-97-VOL2/PDF/011.pdf) +- [Stacked Generalization: when does it work?](http://www.ijcai.org/Proceedings/97-2/011.pdf) - [Stacked Generalization Paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.1533&rep=rep1&type=pdf) @@ -599,7 +599,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Should all Machine Learning be Bayesian?](http://videolectures.net/bark08_ghahramani_samlbb/) - [Tutorial on Bayesian Optimisation for Machine Learning](http://www.iro.umontreal.ca/~bengioy/cifar/NCAP2014-summerschool/slides/Ryan_adams_140814_bayesopt_ncap.pdf) - [Bayesian Reasoning and Deep Learning](http://blog.shakirm.com/2015/10/bayesian-reasoning-and-deep-learning/), [Slides](http://blog.shakirm.com/wp-content/uploads/2015/10/Bayes_Deep.pdf) -- [Bayesian Statistics Made Simple](http://greenteapress.com/thinkbayes/) +- [Bayesian Statistics Made Simple](http://greenteapress.com/wp/think-bayes/) - [Kalman & Bayesian Filters in Python](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python) - [Markov Chain Wikipedia Page](https://en.wikipedia.org/wiki/Markov_chain) From ae7f37d0b918d5d66a2e339dcc0ef50243744478 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Thu, 19 May 2016 12:48:38 +0800 Subject: [PATCH 10/77] Update contributing.md --- contributing.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/contributing.md b/contributing.md index 56e3289..8430b23 100644 --- a/contributing.md +++ b/contributing.md @@ -4,14 +4,14 @@ If you want to contribute to this list (please do), send me a pull request. Sinc Please ensure your pull request adheres to the following guidelines: +- **Please make an individual pull request for each suggestion.** +- The pull request and commit should have a useful title. - Please search previous suggestions before making a new one, as yours may be a duplicate. - Make sure your link has a useful and relevant title. -- Please make an individual pull request for each suggestion. - Please use [title-casing](http://titlecapitalization.com) (AP style). - Please use the following format: `[Useful Title](link)` - Link additions should be added to the bottom of the relevant category. - New categories or improvements to the existing categorization are welcome. - Please Check your spelling and grammar. -- The pull request and commit should have a useful title. Thank you for your suggestions! From 9465ce0f15e27dd485da40e46ecc51868e300fe8 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Thu, 19 May 2016 12:49:47 +0800 Subject: [PATCH 11/77] Update contributing.md --- contributing.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contributing.md b/contributing.md index 8430b23..134caf1 100644 --- a/contributing.md +++ b/contributing.md @@ -12,6 +12,6 @@ Please ensure your pull request adheres to the following guidelines: - Please use the following format: `[Useful Title](link)` - Link additions should be added to the bottom of the relevant category. - New categories or improvements to the existing categorization are welcome. -- Please Check your spelling and grammar. +- Please check your spelling and grammar. Thank you for your suggestions! From 38c138a2209c7f6c0d30ef7f955794e621699136 Mon Sep 17 00:00:00 2001 From: Kevin Markham Date: Fri, 1 Jul 2016 11:38:09 -0400 Subject: [PATCH 12/77] add OpenIntro Statistics textbook --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f539e34..24f0b21 100644 --- a/README.md +++ b/README.md @@ -106,6 +106,7 @@ - [What is an Unbiased Estimator?](https://www.physicsforums.com/threads/what-is-an-unbiased-estimator.547728/) - [Goodness of Fit Explained](https://en.wikipedia.org/wiki/Goodness_of_fit) - [What are QQ Plots?](http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html) +- [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os) - Free PDF textbook ##Useful Blogs From 40c7f2c972f4829cf1ab403232baac2d1e67b282 Mon Sep 17 00:00:00 2001 From: Kevin Markham Date: Fri, 1 Jul 2016 11:42:41 -0400 Subject: [PATCH 13/77] add link to Kaggle Competitions WriteUp section --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 24f0b21..b7e040e 100644 --- a/README.md +++ b/README.md @@ -145,6 +145,7 @@ - [Convolution Neural Networks for EEG detection](http://blog.kaggle.com/2015/10/05/grasp-and-lift-eeg-detection-winners-interview-3rd-place-team-hedj/) - [Facebook Recruiting III Explained](http://alexminnaar.com/tag/kaggle-competitions.html) - [Predicting CTR with Online ML](http://mlwave.com/predicting-click-through-rates-with-online-machine-learning/) +- [How to Rank 10% in Your First Kaggle Competition](https://dnc1994.com/2016/05/rank-10-percent-in-first-kaggle-competition-en/) ##Cheat Sheets From ea30bc6e92d62e572238b6bf4edc237da959e0cf Mon Sep 17 00:00:00 2001 From: Kevin Markham Date: Fri, 1 Jul 2016 11:52:11 -0400 Subject: [PATCH 14/77] add related video link to "ROC and AUC Explained" --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f539e34..18cebef 100644 --- a/README.md +++ b/README.md @@ -156,7 +156,7 @@ - [What is Deviance?](http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart) - [When to choose which machine learning classifier?](http://stackoverflow.com/questions/2595176/when-to-choose-which-machine-learning-classifier) - [What are the advantages of different classification algorithms?](https://www.quora.com/What-are-the-advantages-of-different-classification-algorithms) -- [ROC and AUC Explained](http://www.dataschool.io/roc-curves-and-auc-explained/) +- [ROC and AUC Explained](http://www.dataschool.io/roc-curves-and-auc-explained/) ([related video](https://youtu.be/OAl6eAyP-yo)) - [An introduction to ROC analysis](https://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf) - [Simple guide to confusion matrix terminology](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) From c3ad6dcdc183753a129ab33b464c318d3d368dbc Mon Sep 17 00:00:00 2001 From: Kevin Markham Date: Fri, 1 Jul 2016 11:56:27 -0400 Subject: [PATCH 15/77] add logistic regression guide --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f539e34..e29f360 100644 --- a/README.md +++ b/README.md @@ -194,6 +194,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Residuals in logistic regression](http://stats.stackexchange.com/questions/1432/what-do-the-residuals-in-a-logistic-regression-mean) - [Difference between logit and probit models](http://stats.stackexchange.com/questions/20523/difference-between-logit-and-probit-models#30909), [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression), [Probit Model Wiki](https://en.wikipedia.org/wiki/Probit_model) - [Pseudo R2 for Logistic Regression](http://stats.stackexchange.com/questions/3559/which-pseudo-r2-measure-is-the-one-to-report-for-logistic-regression-cox-s), [How to calculate](http://stats.stackexchange.com/questions/8511/how-to-calculate-pseudo-r2-from-rs-logistic-regression), [Other Details](http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm) +- [Guide to an in-depth understanding of logistic regression](http://www.dataschool.io/guide-to-logistic-regression/) ##Model Validation using Resampling From 958a7105c003ca11c082f657ef26098b208f8d1a Mon Sep 17 00:00:00 2001 From: Kevin Markham Date: Fri, 1 Jul 2016 12:01:05 -0400 Subject: [PATCH 16/77] add link to machine learning videos by Hastie and Tibshirani --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f539e34..b0e6bcf 100644 --- a/README.md +++ b/README.md @@ -67,6 +67,7 @@ - [Machine Learning mistakes to avoid](https://medium.com/@nomadic_mind/new-to-machine-learning-avoid-these-three-mistakes-73258b3848a4#.lih061l3l) - [Statistical Machine Learning Course](http://www.stat.cmu.edu/~larry/=sml/) - [TheAnalyticsEdge edX Notes and Codes](https://github.com/pedrosan/TheAnalyticsEdge) +- [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) ##Interview Resources From d367b54cb2ff9e983149089c55e35d918630986d Mon Sep 17 00:00:00 2001 From: Ho Sun Lee Date: Fri, 29 Jul 2016 12:46:37 -0700 Subject: [PATCH 17/77] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index e9d7191..f722408 100644 --- a/README.md +++ b/README.md @@ -309,6 +309,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Simplified Scikit-learn Style Interface to TensorFlow](https://github.com/tensorflow/skflow) - [Learning TensorFlow GitHub Repo](https://github.com/chetannaik/learning_tensorflow) - [Benchmark TensorFlow GitHub](https://github.com/soumith/convnet-benchmarks/issues/66) + - [Awesome TensorFlow List](https://github.com/jtoy/awesome-tensorflow) From 5f9b3d630cb04f1ca90ee4b79fbac6afaf554fb1 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Mon, 15 Aug 2016 02:30:51 +0800 Subject: [PATCH 18/77] added new links --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index f722408..628e303 100644 --- a/README.md +++ b/README.md @@ -314,6 +314,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - Feed Forward Networks + - [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/) - [Implementing a Neural Network from scratch](http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/), [Code](https://github.com/dennybritz/nn-from-scratch) - [Speeding up your Neural Network with Theano and the gpu](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/), [Code](https://github.com/dennybritz/nn-theano) - [Basic ANN Theory](https://takinginitiative.wordpress.com/2008/04/03/basic-neural-network-tutorial-theory/) @@ -383,6 +384,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - Convolution Networks + - [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) - [Awesome Deep Vision: List of Resources (GitHub)](https://github.com/kjw0612/awesome-deep-vision) - [Intro to CNNs](http://deeplearning4j.org/convolutionalnets.html) - [Understanding CNN for NLP](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/) From 2f0c466513ba92260ebed9b913b7ef8c3017bdc3 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Mon, 15 Aug 2016 02:31:54 +0800 Subject: [PATCH 19/77] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 628e303..c41e0bb 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,7 @@ - [Recurrent Neural Nets, LSTM, GRU](#rnn) - [Restricted Boltzmann Machine, DBNs](#rbm) - [Autoencoders](#auto) - - [Convolution Neural Nets](#cnn) + - [Convolutional Neural Nets](#cnn) - [Natural Language Processing](#nlp) - [Topic Modeling, LDA](#topic) - [Word2Vec](#word2vec) @@ -383,7 +383,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. -- Convolution Networks +- Convolutional Neural Networks - [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) - [Awesome Deep Vision: List of Resources (GitHub)](https://github.com/kjw0612/awesome-deep-vision) - [Intro to CNNs](http://deeplearning4j.org/convolutionalnets.html) From 6c94faf300769072047a4853c890fec56c5c9f3b Mon Sep 17 00:00:00 2001 From: Sudeep Raja Date: Thu, 18 Aug 2016 17:44:50 +0530 Subject: [PATCH 20/77] New Backpropagation derivation The derivation of backpropagation in [Backpropagation Explained](http://home.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html) is wrong, The deltas do not have the differentiation of the activation function. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c41e0bb..f209498 100644 --- a/README.md +++ b/README.md @@ -320,7 +320,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Basic ANN Theory](https://takinginitiative.wordpress.com/2008/04/03/basic-neural-network-tutorial-theory/) - [Role of Bias in Neural Networks](http://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks) - [Choosing number of hidden layers and nodes](http://stackoverflow.com/questions/3345079/estimating-the-number-of-neurons-and-number-of-layers-of-an-artificial-neural-ne),[2](http://stackoverflow.com/questions/10565868/multi-layer-perceptron-mlp-architecture-criteria-for-choosing-number-of-hidde?lq=1),[3](http://stackoverflow.com/questions/9436209/how-to-choose-number-of-hidden-layers-and-nodes-in-neural-network/2#) - - [Backpropagation Explained](http://home.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html) + - [Backpropagation in Matrix Form](http://sudeepraja.github.io/Neural/) - [ANN implemented in C++ | AI Junkie](http://www.ai-junkie.com/ann/evolved/nnt6.html) - [Simple Implementation](http://stackoverflow.com/questions/15395835/simple-multi-layer-neural-network-implementation) - [NN for Beginners](http://www.codeproject.com/Articles/16419/AI-Neural-Network-for-beginners-Part-of) From 791cebd726cdd061aa95485c87fb2bf273a39737 Mon Sep 17 00:00:00 2001 From: Norbert Date: Sun, 4 Sep 2016 09:13:36 +0200 Subject: [PATCH 21/77] Practical XGBoost in Python online course --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index c41e0bb..49118f2 100644 --- a/README.md +++ b/README.md @@ -562,6 +562,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [xgboost tuning kaggle](https://www.kaggle.com/khozzy/rossmann-store-sales/xgboost-parameter-tuning-template/log) - [xgboost vs gbm](https://www.kaggle.com/c/otto-group-product-classification-challenge/forums/t/13012/question-to-experienced-kagglers-and-anyone-who-wants-to-take-a-shot/68296#post68296) - [xgboost survey](https://www.kaggle.com/c/higgs-boson/forums/t/10335/xgboost-post-competition-survey) + - [Practical XGBoost in Python online course (free)](http://education.parrotprediction.teachable.com/courses/practical-xgboost-in-python) - AdaBoost - [AdaBoost Wiki](https://en.wikipedia.org/wiki/AdaBoost), [Python Code](https://gist.github.com/tristanwietsma/5486024) - [AdaBoost Sparse Input Support](http://hamzehal.blogspot.com/2014/06/adaboost-sparse-input-support.html) From 3e7dc52af2bc7df5778511bcd10f0cadfb777043 Mon Sep 17 00:00:00 2001 From: ZuzooVn Date: Mon, 24 Oct 2016 13:50:54 +0700 Subject: [PATCH 22/77] Added "Machine Learning for Software Engineers" --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index e14c1a1..96ebc90 100644 --- a/README.md +++ b/README.md @@ -51,6 +51,7 @@ ##Miscellaneous +- [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) - [A curated list of awesome Machine Learning frameworks, libraries and software](https://github.com/josephmisiti/awesome-machine-learning) - [A curated list of awesome data visualization libraries and resources.](https://github.com/fasouto/awesome-dataviz) - [An awesome Data Science repository to learn and apply for real world problems](https://github.com/okulbilisim/awesome-datascience) From b31c65e847c78d978466408f4a34d552ae41189f Mon Sep 17 00:00:00 2001 From: Brandon Amos Date: Sun, 30 Oct 2016 20:47:07 -0400 Subject: [PATCH 23/77] Update link to 'Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables' --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 96ebc90..6d09a87 100644 --- a/README.md +++ b/README.md @@ -59,7 +59,7 @@ - [Machine Learning FAQs on Cross Validated](http://stats.stackexchange.com/questions/tagged/machine-learning) - [List of Machine Learning University Courses](https://github.com/prakhar1989/awesome-courses#machine-learning) - [Machine Learning algorithms that you should always have a strong understanding of](https://www.quora.com/What-are-some-Machine-Learning-algorithms-that-you-should-always-have-a-strong-understanding-of-and-why) -- [Differnce between Linearly Independent, Orthogonal, and Uncorrelated Variables](https://www.psych.umn.edu/faculty/waller/classes/FA2010/Readings/rodgers.pdf) +- [Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables](http://terpconnect.umd.edu/~bmomen/BIOM621/LineardepCorrOrthogonal.pdf) - [List of Machine Learning Concepts](https://en.wikipedia.org/wiki/List_of_machine_learning_concepts) - [Slides on Several Machine Learning Topics](http://www.slideshare.net/pierluca.lanzi/presentations) - [MIT Machine Learning Lecture Slides](http://www.ai.mit.edu/courses/6.867-f04/lectures.html) From 3957e027c55597762ca80db85332e6823c11a7d9 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Tue, 8 Nov 2016 09:09:28 -0600 Subject: [PATCH 24/77] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 6d09a87..c71c071 100644 --- a/README.md +++ b/README.md @@ -52,6 +52,7 @@ ##Miscellaneous - [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) +- [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning) - [A curated list of awesome Machine Learning frameworks, libraries and software](https://github.com/josephmisiti/awesome-machine-learning) - [A curated list of awesome data visualization libraries and resources.](https://github.com/fasouto/awesome-dataviz) - [An awesome Data Science repository to learn and apply for real world problems](https://github.com/okulbilisim/awesome-datascience) From f75439514e5087770cbd2f722a095312200a8b3a Mon Sep 17 00:00:00 2001 From: Sudip Bhandari Date: Fri, 16 Dec 2016 17:15:36 +0530 Subject: [PATCH 25/77] Added J Alammar's blog This blog has an interactive visual illustration of neural networks concepts --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index c71c071..515540d 100644 --- a/README.md +++ b/README.md @@ -130,6 +130,8 @@ - [Variance Explained](http://varianceexplained.org/) - David Robinson's Blog - [AI Junkie](http://www.ai-junkie.com/) - a blog about Artificial Intellingence - [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/)- Making deep learning accessible +- [A Visual and Interactive Guide to the Basics of Neural Networks](http://jalammar.github.io/)- J Alammar Explorations in touchable pixels and intelligent androids + ##Resources on Quora From 779a1f12319ef655c4700d80e79468abdfc076e5 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Fri, 16 Dec 2016 20:13:22 +0800 Subject: [PATCH 26/77] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 515540d..208f4fe 100644 --- a/README.md +++ b/README.md @@ -130,7 +130,7 @@ - [Variance Explained](http://varianceexplained.org/) - David Robinson's Blog - [AI Junkie](http://www.ai-junkie.com/) - a blog about Artificial Intellingence - [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/)- Making deep learning accessible -- [A Visual and Interactive Guide to the Basics of Neural Networks](http://jalammar.github.io/)- J Alammar Explorations in touchable pixels and intelligent androids +- [J Alammar's Blog](http://jalammar.github.io/)- Blog posts about Machine Learning and Neural Nets From c214c15eafe281833e7f97f7dd82cf1c8b068994 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Sun, 1 Jan 2017 22:56:20 +0800 Subject: [PATCH 27/77] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 208f4fe..5db8bb9 100644 --- a/README.md +++ b/README.md @@ -413,6 +413,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Topic Modeling](https://en.wikipedia.org/wiki/Topic_model) - [LDA](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation), [LSA](https://en.wikipedia.org/wiki/Latent_semantic_analysis), [Probabilistic LSA](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) + - [What is a good explanation of Latent Dirichlet Allocation?](https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation) - [Awesome LDA Explanation!](http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/). [Another good explanation](http://confusedlanguagetech.blogspot.in/2012/07/jordan-boyd-graber-and-philip-resnik.html) - [The LDA Buffet- Intuitive Explanation](http://www.matthewjockers.net/2011/09/29/the-lda-buffet-is-now-open-or-latent-dirichlet-allocation-for-english-majors/) - [Difference between LSI and LDA](https://www.quora.com/Whats-the-difference-between-Latent-Semantic-Indexing-LSI-and-Latent-Dirichlet-Allocation-LDA) From ba7476825f5818551821383398ed1e47b7804e4b Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Sun, 8 Jan 2017 00:23:20 +0800 Subject: [PATCH 28/77] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 5db8bb9..8209288 100644 --- a/README.md +++ b/README.md @@ -81,6 +81,7 @@ ##Artificial Intelligence - [Awesome Artificial Intelligence (GitHub Repo)](https://github.com/owainlewis/awesome-artificial-intelligence) +- [UC Berkeley CS188 Intro to AI](http://ai.berkeley.edu/home.html), [Lecture Videos](http://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM) - [edX course | Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) - [Udacity Course | Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) - [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) From 67b0a38943a62821c97bfbdc2892e0ef742c7aa3 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Sat, 14 Jan 2017 15:29:57 +0800 Subject: [PATCH 29/77] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 8209288..205a9e0 100644 --- a/README.md +++ b/README.md @@ -82,6 +82,7 @@ ##Artificial Intelligence - [Awesome Artificial Intelligence (GitHub Repo)](https://github.com/owainlewis/awesome-artificial-intelligence) - [UC Berkeley CS188 Intro to AI](http://ai.berkeley.edu/home.html), [Lecture Videos](http://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM) +- [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi) - [edX course | Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) - [Udacity Course | Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) - [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) From 04c13a4dd8d2651320db8670b23131df892e6b9c Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Sat, 14 Jan 2017 15:35:03 +0800 Subject: [PATCH 30/77] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 205a9e0..efae57e 100644 --- a/README.md +++ b/README.md @@ -82,7 +82,7 @@ ##Artificial Intelligence - [Awesome Artificial Intelligence (GitHub Repo)](https://github.com/owainlewis/awesome-artificial-intelligence) - [UC Berkeley CS188 Intro to AI](http://ai.berkeley.edu/home.html), [Lecture Videos](http://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM) -- [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi) +- [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/) - [edX course | Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) - [Udacity Course | Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) - [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) From 6794976129596214336d242ebae3d4224abd6670 Mon Sep 17 00:00:00 2001 From: nitin-tm Date: Mon, 30 Jan 2017 00:27:58 +0530 Subject: [PATCH 31/77] Easiest Machine Learning Blog --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index efae57e..24db67c 100644 --- a/README.md +++ b/README.md @@ -133,7 +133,7 @@ - [AI Junkie](http://www.ai-junkie.com/) - a blog about Artificial Intellingence - [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/)- Making deep learning accessible - [J Alammar's Blog](http://jalammar.github.io/)- Blog posts about Machine Learning and Neural Nets - +- [Adam Geitgey](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.f7vwrtfne) - Easiest Introduction to machine learning ##Resources on Quora From cef5a9659a30aa24e71c2eabd1d8efd50f28cd2a Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Sun, 29 Jan 2017 14:29:26 -0800 Subject: [PATCH 32/77] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 24db67c..f729f6e 100644 --- a/README.md +++ b/README.md @@ -73,6 +73,7 @@ ##Interview Resources +- [41 Essential Machine Learning Interview Questions (with answers)](https://www.springboard.com/blog/machine-learning-interview-questions/) - [How can a computer science graduate student prepare himself for data scientist interviews?](https://www.quora.com/How-can-a-computer-science-graduate-student-prepare-himself-for-data-scientist-machine-learning-intern-interviews) - [How do I learn Machine Learning?](https://www.quora.com/How-do-I-learn-machine-learning-1) - [FAQs about Data Science Interviews](https://www.quora.com/topic/Data-Science-Interviews/faq) @@ -432,6 +433,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - word2vec - [Google word2vec](https://code.google.com/archive/p/word2vec) - [Bag of Words Model Wiki](https://en.wikipedia.org/wiki/Bag-of-words_model) + - [word2vec Tutorial](https://rare-technologies.com/word2vec-tutorial/) - [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) - [Skip Gram Model Tutorial](http://alexminnaar.com/word2vec-tutorial-part-i-the-skip-gram-model.html), [CBoW Model](http://alexminnaar.com/word2vec-tutorial-part-ii-the-continuous-bag-of-words-model.html) - [Word Vectors Kaggle Tutorial Python](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors), [Part 2](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-3-more-fun-with-word-vectors) From 80b158dfb6b5fa25898a765bf4f914e19aa9f38d Mon Sep 17 00:00:00 2001 From: Hendricks Date: Tue, 31 Jan 2017 02:04:44 -1000 Subject: [PATCH 33/77] Added 'Have Fun With Machine Learning' --- README.md | 85 ++++++++++++++++++++++++++++--------------------------- 1 file changed, 43 insertions(+), 42 deletions(-) diff --git a/README.md b/README.md index f729f6e..f9b51a6 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Machine Learning & Deep Learning Tutorials [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) -- This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this [list](https://github.com/sindresorhus/awesome). +- This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this [list](https://github.com/sindresorhus/awesome). - If you want to contribute to this list, please read [Contributing Guidelines](https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/contributing.md). @@ -23,7 +23,7 @@ - [Linear Regression](#linear) - [Logistic Regression](#logistic) - [Model Validation using Resampling](#validation) - - [Cross Validation](#cross) + - [Cross Validation](#cross) - [Bootstraping](#boot) - [Deep Learning](#deep) - [Frameworks](#frame) @@ -63,13 +63,14 @@ - [Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables](http://terpconnect.umd.edu/~bmomen/BIOM621/LineardepCorrOrthogonal.pdf) - [List of Machine Learning Concepts](https://en.wikipedia.org/wiki/List_of_machine_learning_concepts) - [Slides on Several Machine Learning Topics](http://www.slideshare.net/pierluca.lanzi/presentations) -- [MIT Machine Learning Lecture Slides](http://www.ai.mit.edu/courses/6.867-f04/lectures.html) +- [MIT Machine Learning Lecture Slides](http://www.ai.mit.edu/courses/6.867-f04/lectures.html) - [Comparison Supervised Learning Algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/) - [Learning Data Science Fundamentals](http://www.dataschool.io/learning-data-science-fundamentals/) - [Machine Learning mistakes to avoid](https://medium.com/@nomadic_mind/new-to-machine-learning-avoid-these-three-mistakes-73258b3848a4#.lih061l3l) - [Statistical Machine Learning Course](http://www.stat.cmu.edu/~larry/=sml/) - [TheAnalyticsEdge edX Notes and Codes](https://github.com/pedrosan/TheAnalyticsEdge) - [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) +- [Have Fun With Machine Learning](https://github.com/humphd/have-fun-with-machine-learning) ##Interview Resources @@ -91,7 +92,7 @@ ##Genetic Algorithms - [Genetic Algorithms Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm) -- [Simple Implementation of Genetic Algorithms in Python (Part 1)](http://outlace.com/Simple-Genetic-Algorithm-in-15-lines-of-Python/), [Part 2](http://outlace.com/Simple-Genetic-Algorithm-Python-Addendum/) +- [Simple Implementation of Genetic Algorithms in Python (Part 1)](http://outlace.com/Simple-Genetic-Algorithm-in-15-lines-of-Python/), [Part 2](http://outlace.com/Simple-Genetic-Algorithm-Python-Addendum/) - [Genetic Algorithms vs Artificial Neural Networks](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks) - [Genetic Algorithms Explained in Plain English](http://www.ai-junkie.com/ga/intro/gat1.html) - [Genetic Programming](https://en.wikipedia.org/wiki/Genetic_programming) @@ -116,7 +117,7 @@ ##Useful Blogs -- [Edwin Chen's Blog](http://blog.echen.me/) - A blog about Math, stats, ML, crowdsourcing, data science +- [Edwin Chen's Blog](http://blog.echen.me/) - A blog about Math, stats, ML, crowdsourcing, data science - [The Data School Blog](http://www.dataschool.io/) - Data science for beginners! - [ML Wave](http://mlwave.com/) - A blog for Learning Machine Learning - [Andrej Karpathy](http://karpathy.github.io/) - A blog about Deep Learning and Data Science in general @@ -133,7 +134,7 @@ - [Variance Explained](http://varianceexplained.org/) - David Robinson's Blog - [AI Junkie](http://www.ai-junkie.com/) - a blog about Artificial Intellingence - [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/)- Making deep learning accessible -- [J Alammar's Blog](http://jalammar.github.io/)- Blog posts about Machine Learning and Neural Nets +- [J Alammar's Blog](http://jalammar.github.io/)- Blog posts about Machine Learning and Neural Nets - [Adam Geitgey](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.f7vwrtfne) - Easiest Introduction to machine learning @@ -149,7 +150,7 @@ ##Kaggle Competitions WriteUp -- [How to almost win Kaggle Competitions](https://yanirseroussi.com/2014/08/24/how-to-almost-win-kaggle-competitions/) +- [How to almost win Kaggle Competitions](https://yanirseroussi.com/2014/08/24/how-to-almost-win-kaggle-competitions/) - [Convolution Neural Networks for EEG detection](http://blog.kaggle.com/2015/10/05/grasp-and-lift-eeg-detection-winners-interview-3rd-place-team-hedj/) - [Facebook Recruiting III Explained](http://alexminnaar.com/tag/kaggle-competitions.html) - [Predicting CTR with Online ML](http://mlwave.com/predicting-click-through-rates-with-online-machine-learning/) @@ -202,7 +203,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Geometric Intuition of Logistic Regression](http://florianhartl.com/logistic-regression-geometric-intuition.html) - [Obtaining predicted categories (choosing threshold)](http://stats.stackexchange.com/questions/25389/obtaining-predicted-values-y-1-or-0-from-a-logistic-regression-model-fit) - [Residuals in logistic regression](http://stats.stackexchange.com/questions/1432/what-do-the-residuals-in-a-logistic-regression-mean) -- [Difference between logit and probit models](http://stats.stackexchange.com/questions/20523/difference-between-logit-and-probit-models#30909), [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression), [Probit Model Wiki](https://en.wikipedia.org/wiki/Probit_model) +- [Difference between logit and probit models](http://stats.stackexchange.com/questions/20523/difference-between-logit-and-probit-models#30909), [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression), [Probit Model Wiki](https://en.wikipedia.org/wiki/Probit_model) - [Pseudo R2 for Logistic Regression](http://stats.stackexchange.com/questions/3559/which-pseudo-r2-measure-is-the-one-to-report-for-logistic-regression-cox-s), [How to calculate](http://stats.stackexchange.com/questions/8511/how-to-calculate-pseudo-r2-from-rs-logistic-regression), [Other Details](http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm) - [Guide to an in-depth understanding of logistic regression](http://www.dataschool.io/guide-to-logistic-regression/) @@ -230,8 +231,8 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [CV for detecting and preventing Overfitting](http://www.autonlab.org/tutorials/overfit10.pdf) - [How does CV overcome the Overfitting Problem](http://stats.stackexchange.com/questions/9053/how-does-cross-validation-overcome-the-overfitting-problem) - - + + - [Bootstrapping](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)) - [Why Bootstrapping Works?](http://stats.stackexchange.com/questions/26088/explaining-to-laypeople-why-bootstrapping-works) @@ -239,7 +240,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Example of Bootstapping](http://statistics.about.com/od/Applications/a/Example-Of-Bootstrapping.htm) - [Understanding Bootstapping for Validation and Model Selection](http://stats.stackexchange.com/questions/14516/understanding-bootstrapping-for-validation-and-model-selection?rq=1) - [Cross Validation vs Bootstrap to estimate prediction error](http://stats.stackexchange.com/questions/18348/differences-between-cross-validation-and-bootstrapping-to-estimate-the-predictio), [Cross-validation vs .632 bootstrapping to evaluate classification performance](http://stats.stackexchange.com/questions/71184/cross-validation-or-bootstrapping-to-evaluate-classification-performance) - + ##Deep Learning @@ -270,7 +271,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Artificial Neural Networks Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks) - [Neural Networks FAQs on Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50) - [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html) -- [Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/) +- [Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/) - Neural Machine Translation - [Introduction to Neural Machine Translation with GPUs (part 1)](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) @@ -283,7 +284,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Deep Learning Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/) - [Theano](https://en.wikipedia.org/wiki/Theano_(software)) - - [Website](http://deeplearning.net/software/theano/) + - [Website](http://deeplearning.net/software/theano/) - [Theano Introduction](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/) - [Theano Tutorial](http://outlace.com/Beginner-Tutorial-Theano/) - [Good Theano Tutorial](http://deeplearning.net/software/theano/tutorial/) @@ -296,28 +297,28 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [DBNs using Theano](http://deeplearning.net/tutorial/DBN.html#dbn) - [All Codes](https://github.com/lisa-lab/DeepLearningTutorials) - [Deep Learning Implementation Tutorials - Keras and Lasagne](https://github.com/vict0rsch/deep_learning/) - + - [Torch](http://torch.ch/) - [Torch ML Tutorial](http://code.madbits.com/wiki/doku.php), [Code](https://github.com/torch/tutorials) - [Intro to Torch](http://ml.informatik.uni-freiburg.de/_media/teaching/ws1415/presentation_dl_lect3.pdf) - [Learning Torch GitHub Repo](https://github.com/chetannaik/learning_torch) - [Awesome-Torch (Repository on GitHub)](https://github.com/carpedm20/awesome-torch) - [Machine Learning using Torch Oxford Univ](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/), [Code](https://github.com/oxford-cs-ml-2015) - - [Torch Internals Overview](https://apaszke.github.io/torch-internals.html) + - [Torch Internals Overview](https://apaszke.github.io/torch-internals.html) - [Torch Cheatsheet](https://github.com/torch/torch7/wiki/Cheatsheet) - [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) - + - Caffe - [Deep Learning for Computer Vision with Caffe and cuDNN](https://devblogs.nvidia.com/parallelforall/deep-learning-computer-vision-caffe-cudnn/) - - - TensorFlow + + - TensorFlow - [Website](http://tensorflow.org/) - [TensorFlow Examples for Beginners](https://github.com/aymericdamien/TensorFlow-Examples) - [Simplified Scikit-learn Style Interface to TensorFlow](https://github.com/tensorflow/skflow) - [Learning TensorFlow GitHub Repo](https://github.com/chetannaik/learning_tensorflow) - [Benchmark TensorFlow GitHub](https://github.com/soumith/convnet-benchmarks/issues/66) - [Awesome TensorFlow List](https://github.com/jtoy/awesome-tensorflow) - + - Feed Forward Networks @@ -350,7 +351,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Music generation using RNNs (Keras)](https://github.com/MattVitelli/GRUV) - [Using RNN to create on-the-fly dialogue (Keras)](http://neuralniche.com/post/tutorial/) - Long Short Term Memory (LSTM) - - [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) + - [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) - [LSTM explained](https://apaszke.github.io/lstm-explained.html) - [Beginner’s Guide to LSTM](http://deeplearning4j.org/lstm.html) - [Implementing LSTM from scratch](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/), [Python/Theano code](https://github.com/dennybritz/rnn-tutorial-gru-lstm) @@ -364,8 +365,8 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Torch code for Visual Question Answering using a CNN+LSTM model](https://github.com/abhshkdz/neural-vqa) - Gated Recurrent Units (GRU) - [LSTM vs GRU](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/) - - + + - [Recursive Neural Network (not Recurrent)](https://en.wikipedia.org/wiki/Recursive_neural_network) - [Recursive Neural Tensor Network (RNTN)](http://deeplearning4j.org/recursiveneuraltensornetwork.html) @@ -373,7 +374,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - Restricted Boltzmann Machine - - [Beginner's Guide about RBMs](http://deeplearning4j.org/restrictedboltzmannmachine.html) + - [Beginner's Guide about RBMs](http://deeplearning4j.org/restrictedboltzmannmachine.html) - [Another Good Tutorial](http://deeplearning.net/tutorial/rbm.html) - [Introduction to RBMs](http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/) - [Hinton's Guide to Training RBMs](https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf) @@ -387,12 +388,12 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Deep Autoencoders Tutorial](http://deeplearning4j.org/deepautoencoder.html) - [Denoising Autoencoders](http://deeplearning.net/tutorial/dA.html), [Theano Code](http://deeplearning.net/tutorial/code/dA.py) - [Stacked Denoising Autoencoders](http://deeplearning.net/tutorial/SdA.html#sda) - + - Convolutional Neural Networks - [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) - - [Awesome Deep Vision: List of Resources (GitHub)](https://github.com/kjw0612/awesome-deep-vision) + - [Awesome Deep Vision: List of Resources (GitHub)](https://github.com/kjw0612/awesome-deep-vision) - [Intro to CNNs](http://deeplearning4j.org/convolutionalnets.html) - [Understanding CNN for NLP](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/) - [Stanford Notes](http://vision.stanford.edu/teaching/cs231n/), [Codes](http://cs231n.github.io/), [GitHub](https://github.com/cs231n/cs231n.github.io) @@ -412,9 +413,9 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [NLP from Scratch | Google Paper](https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/35671.pdf) - [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) - [Bag of Words](https://en.wikipedia.org/wiki/Bag-of-words_model) - - [Classification text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) + - [Classification text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) -- [Topic Modeling](https://en.wikipedia.org/wiki/Topic_model) +- [Topic Modeling](https://en.wikipedia.org/wiki/Topic_model) - [LDA](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation), [LSA](https://en.wikipedia.org/wiki/Latent_semantic_analysis), [Probabilistic LSA](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) - [What is a good explanation of Latent Dirichlet Allocation?](https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation) - [Awesome LDA Explanation!](http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/). [Another good explanation](http://confusedlanguagetech.blogspot.in/2012/07/jordan-boyd-graber-and-philip-resnik.html) @@ -444,13 +445,13 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) - Text Clustering - - [How string clustering works](http://stackoverflow.com/questions/8196371/how-clustering-works-especially-string-clustering) + - [How string clustering works](http://stackoverflow.com/questions/8196371/how-clustering-works-especially-string-clustering) - [Levenshtein distance for measuring the difference between two sequences](https://en.wikipedia.org/wiki/Levenshtein_distance) - [Text clustering with Levenshtein distances](http://stackoverflow.com/questions/21511801/text-clustering-with-levenshtein-distances) - Text Classification - - [Classification Text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) - + - [Classification Text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) + - [Language learning with NLP and reinforcement learning](http://blog.dennybritz.com/2015/09/11/reimagining-language-learning-with-nlp-and-reinforcement-learning/) - [Kaggle Tutorial Bag of Words and Word vectors](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words), [Part 2](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors), [Part 3](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-3-more-fun-with-word-vectors) - [What would Shakespeare say (NLP Tutorial)](https://gigadom.wordpress.com/2015/10/02/natural-language-processing-what-would-shakespeare-say/) @@ -479,13 +480,13 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Optimization Algorithms in Support Vector Machines](http://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf) - [Variable Importance from SVM](http://stats.stackexchange.com/questions/2179/variable-importance-from-svm) - Software - - [LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) + - [LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) - [Intro to SVM in R](http://cbio.ensmp.fr/~jvert/svn/tutorials/practical/svmbasic/svmbasic_notes.pdf) - Kernels - [What are Kernels in ML and SVM?](https://www.quora.com/What-are-Kernels-in-Machine-Learning-and-SVM) - - [Intuition Behind Gaussian Kernel in SVMs?](https://www.quora.com/Support-Vector-Machines/What-is-the-intuition-behind-Gaussian-kernel-in-SVM) + - [Intuition Behind Gaussian Kernel in SVMs?](https://www.quora.com/Support-Vector-Machines/What-is-the-intuition-behind-Gaussian-kernel-in-SVM) - Probabilities post SVM - - [Platt's Probabilistic Outputs for SVM](http://www.csie.ntu.edu.tw/~htlin/paper/doc/plattprob.pdf) + - [Platt's Probabilistic Outputs for SVM](http://www.csie.ntu.edu.tw/~htlin/paper/doc/plattprob.pdf) - [Platt Calibration Wiki](https://en.wikipedia.org/wiki/Platt_scaling) - [Why use Platts Scaling](http://stats.stackexchange.com/questions/5196/why-use-platts-scaling) - [Classifier Classification with Platt's Scaling](http://fastml.com/classifier-calibration-with-platts-scaling-and-isotonic-regression/) @@ -526,19 +527,19 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Does rpart use multivariate splits by default?](http://stats.stackexchange.com/questions/4356/does-rpart-use-multivariate-splits-by-default) - [FAQs about Recursive Partitioning](http://stats.stackexchange.com/questions/tagged/rpart) - CTREE - - [party package in R](https://cran.r-project.org/web/packages/party/party.pdf) + - [party package in R](https://cran.r-project.org/web/packages/party/party.pdf) - [Show volumne in each node using ctree in R](http://stackoverflow.com/questions/13772715/show-volume-in-each-node-using-ctree-plot-in-r) - [How to extract tree structure from ctree function?](http://stackoverflow.com/questions/8675664/how-to-extract-tree-structure-from-ctree-function) - CHAID - - [Wikipedia Artice on CHAID](https://en.wikipedia.org/wiki/CHAID) + - [Wikipedia Artice on CHAID](https://en.wikipedia.org/wiki/CHAID) - [Basic Introduction to CHAID](https://smartdrill.com/Introduction-to-CHAID.html) - [Good Tutorial on CHAID](http://www.statsoft.com/Textbook/CHAID-Analysis) - MARS - - [Wikipedia Article on MARS](https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines) + - [Wikipedia Article on MARS](https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines) - Probabilistic Decision Trees - - [Bayesian Learning in Probabilistic Decision Trees](http://www.stats.org.uk/bayesian/Jordan.pdf) + - [Bayesian Learning in Probabilistic Decision Trees](http://www.stats.org.uk/bayesian/Jordan.pdf) - [Probabilistic Trees Research Paper](http://people.stern.nyu.edu/adamodar/pdfiles/papers/probabilistic.pdf) - + ##Random Forest / Bagging - [Awesome Random Forest (GitHub)**](https://github.com/kjw0612/awesome-random-forest) @@ -554,7 +555,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Some Questions for R implementation](http://stackoverflow.com/questions/20537186/getting-predictions-after-rfimpute), [2](http://stats.stackexchange.com/questions/81609/whether-preprocessing-is-needed-before-prediction-using-finalmodel-of-randomfore), [3](http://stackoverflow.com/questions/17059432/random-forest-package-in-r-shows-error-during-prediction-if-there-are-new-fact) -##Boosting +##Boosting - [Boosting for Better Predictions](http://www.datasciencecentral.com/profiles/blogs/boosting-algorithms-for-better-predictions) - [Boosting Wikipedia Page](https://en.wikipedia.org/wiki/Boosting_(machine_learning)) - [Introduction to Boosted Trees | Tianqi Chen](https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf) @@ -594,7 +595,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [How are classifications merged in an ensemble classifier?](http://stats.stackexchange.com/questions/21502/how-are-classifications-merged-in-an-ensemble-classifier) -##Stacking Models +##Stacking Models - [Stacking, Blending and Stacked Generalization](http://www.chioka.in/stacking-blending-and-stacked-generalization/) - [Stacked Generalization (Stacking)](http://machine-learning.martinsewell.com/ensembles/stacking/) - [Stacked Generalization: when does it work?](http://www.ijcai.org/Proceedings/97-2/011.pdf) @@ -624,13 +625,13 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. ##Semi Supervised Learning - [Wikipedia article on Semi Supervised Learning](https://en.wikipedia.org/wiki/Semi-supervised_learning) -- [Tutorial on Semi Supervised Learning](http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf) +- [Tutorial on Semi Supervised Learning](http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf) - [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) - [Taxonomy](http://is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/taxo_[0].pdf) - [Video Tutorial Weka](https://www.youtube.com/watch?v=sWxcIjZFGNM) - [Unsupervised, Supervised and Semi Supervised learning](http://stats.stackexchange.com/questions/517/unsupervised-supervised-and-semi-supervised-learning) - [Research Papers 1](http://mlg.eng.cam.ac.uk/zoubin/papers/zglactive.pdf), [2](http://mlg.eng.cam.ac.uk/zoubin/papers/zgl.pdf), [3](http://icml.cc/2012/papers/616.pdf) - + From dab568ddcf5d6584dbcd65367ae671ed88c67733 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Fri, 3 Feb 2017 19:30:11 -0800 Subject: [PATCH 34/77] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f729f6e..f970879 100644 --- a/README.md +++ b/README.md @@ -317,6 +317,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Learning TensorFlow GitHub Repo](https://github.com/chetannaik/learning_tensorflow) - [Benchmark TensorFlow GitHub](https://github.com/soumith/convnet-benchmarks/issues/66) - [Awesome TensorFlow List](https://github.com/jtoy/awesome-tensorflow) + - [TensorFlow Book](https://github.com/BinRoot/TensorFlow-Book) From 663ff8588ccb7f415e027f3fc86125fd85630a93 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Sat, 4 Feb 2017 15:17:28 -0800 Subject: [PATCH 35/77] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f970879..486d6a7 100644 --- a/README.md +++ b/README.md @@ -87,6 +87,7 @@ - [edX course | Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) - [Udacity Course | Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) - [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) +- [Berkeley AI Materials](http://ai.berkeley.edu/lecture_videos.html) ##Genetic Algorithms From 97d36e61881f75d5e6cdcd756a489ae0d9f00ef5 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Sat, 4 Feb 2017 15:18:13 -0800 Subject: [PATCH 36/77] Update README.md --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index 486d6a7..f970879 100644 --- a/README.md +++ b/README.md @@ -87,7 +87,6 @@ - [edX course | Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) - [Udacity Course | Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) - [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) -- [Berkeley AI Materials](http://ai.berkeley.edu/lecture_videos.html) ##Genetic Algorithms From 347b787ffcbd17c77933ccc3f5efbd26c969c259 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Sat, 4 Feb 2017 16:05:41 -0800 Subject: [PATCH 37/77] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index f970879..ab92eb7 100644 --- a/README.md +++ b/README.md @@ -313,6 +313,8 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - TensorFlow - [Website](http://tensorflow.org/) - [TensorFlow Examples for Beginners](https://github.com/aymericdamien/TensorFlow-Examples) + - [Stanford Tensorflow for Deep Learning Research Course](https://web.stanford.edu/class/cs20si/syllabus.html) + - [GitHub Repo](https://github.com/chiphuyen/tf-stanford-tutorials) - [Simplified Scikit-learn Style Interface to TensorFlow](https://github.com/tensorflow/skflow) - [Learning TensorFlow GitHub Repo](https://github.com/chetannaik/learning_tensorflow) - [Benchmark TensorFlow GitHub](https://github.com/soumith/convnet-benchmarks/issues/66) From dc6d8d49ef84667a8353079620112aa735fbacb5 Mon Sep 17 00:00:00 2001 From: AMIT SHEKHAR Date: Tue, 7 Mar 2017 10:32:22 +0530 Subject: [PATCH 38/77] Add Android TensorFlow Machine Learning Example --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index b52764d..4a968bd 100644 --- a/README.md +++ b/README.md @@ -321,6 +321,8 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Benchmark TensorFlow GitHub](https://github.com/soumith/convnet-benchmarks/issues/66) - [Awesome TensorFlow List](https://github.com/jtoy/awesome-tensorflow) - [TensorFlow Book](https://github.com/BinRoot/TensorFlow-Book) + - [Android TensorFlow Machine Learning Example](https://blog.mindorks.com/android-tensorflow-machine-learning-example-ff0e9b2654cc) + - [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMachineLearningExample) - Feed Forward Networks From 31ded4ab07ed3809cdea57942ddfc894bbf04b24 Mon Sep 17 00:00:00 2001 From: AMIT SHEKHAR Date: Fri, 17 Mar 2017 10:15:23 +0530 Subject: [PATCH 39/77] Add Custom Model Example For Android --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 4a968bd..1e86e88 100644 --- a/README.md +++ b/README.md @@ -323,6 +323,8 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [TensorFlow Book](https://github.com/BinRoot/TensorFlow-Book) - [Android TensorFlow Machine Learning Example](https://blog.mindorks.com/android-tensorflow-machine-learning-example-ff0e9b2654cc) - [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMachineLearningExample) + - [Creating Custom Model For Android Using TensorFlow](https://blog.mindorks.com/creating-custom-model-for-android-using-tensorflow-3f963d270bfb) + - [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMNISTExample) - Feed Forward Networks From e7d1b2821c0368fa8da2a2be3ce10a38ca581d81 Mon Sep 17 00:00:00 2001 From: Manash Kumar Mandal Date: Fri, 24 Mar 2017 11:45:48 +0600 Subject: [PATCH 40/77] Update README.md --- README.md | 60 +++++++++++++++++++++++++++++-------------------------- 1 file changed, 32 insertions(+), 28 deletions(-) diff --git a/README.md b/README.md index 4a968bd..5e0af1f 100644 --- a/README.md +++ b/README.md @@ -50,7 +50,7 @@ - [Other Useful Tutorials](#other) -##Miscellaneous +## Miscellaneous - [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) - [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning) - [A curated list of awesome Machine Learning frameworks, libraries and software](https://github.com/josephmisiti/awesome-machine-learning) @@ -73,7 +73,7 @@ - [Have Fun With Machine Learning](https://github.com/humphd/have-fun-with-machine-learning) -##Interview Resources +## Interview Resources - [41 Essential Machine Learning Interview Questions (with answers)](https://www.springboard.com/blog/machine-learning-interview-questions/) - [How can a computer science graduate student prepare himself for data scientist interviews?](https://www.quora.com/How-can-a-computer-science-graduate-student-prepare-himself-for-data-scientist-machine-learning-intern-interviews) - [How do I learn Machine Learning?](https://www.quora.com/How-do-I-learn-machine-learning-1) @@ -81,7 +81,7 @@ - [What are the key skills of a data scientist?](https://www.quora.com/What-are-the-key-skills-of-a-data-scientist) -##Artificial Intelligence +## Artificial Intelligence - [Awesome Artificial Intelligence (GitHub Repo)](https://github.com/owainlewis/awesome-artificial-intelligence) - [UC Berkeley CS188 Intro to AI](http://ai.berkeley.edu/home.html), [Lecture Videos](http://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM) - [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/) @@ -90,7 +90,7 @@ - [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) -##Genetic Algorithms +## Genetic Algorithms - [Genetic Algorithms Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm) - [Simple Implementation of Genetic Algorithms in Python (Part 1)](http://outlace.com/Simple-Genetic-Algorithm-in-15-lines-of-Python/), [Part 2](http://outlace.com/Simple-Genetic-Algorithm-Python-Addendum/) - [Genetic Algorithms vs Artificial Neural Networks](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks) @@ -100,7 +100,7 @@ - [Genetic Alogorithms vs Genetic Programming (Quora)](https://www.quora.com/Whats-the-difference-between-Genetic-Algorithms-and-Genetic-Programming), [StackOverflow](http://stackoverflow.com/questions/3819977/what-are-the-differences-between-genetic-algorithms-and-genetic-programming) -##Statistics +## Statistics - [Stat Trek Website](http://stattrek.com/) - A dedicated website to teach yourselves Statistics - [Learn Statistics Using Python](https://github.com/rouseguy/intro2stats) - Learn Statistics using an application-centric programming approach - [Statistics for Hackers | Slides | @jakevdp](https://speakerdeck.com/jakevdp/statistics-for-hackers) - Slides by Jake VanderPlas @@ -116,7 +116,7 @@ - [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os) - Free PDF textbook -##Useful Blogs +## Useful Blogs - [Edwin Chen's Blog](http://blog.echen.me/) - A blog about Math, stats, ML, crowdsourcing, data science - [The Data School Blog](http://www.dataschool.io/) - Data science for beginners! - [ML Wave](http://mlwave.com/) - A blog for Learning Machine Learning @@ -138,7 +138,7 @@ - [Adam Geitgey](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.f7vwrtfne) - Easiest Introduction to machine learning -##Resources on Quora +## Resources on Quora - [Most Viewed Machine Learning writers](https://www.quora.com/topic/Machine-Learning/writers) - [Data Science Topic on Quora](https://www.quora.com/Data-Science) - [William Chen's Answers](https://www.quora.com/William-Chen-6/answers) @@ -149,7 +149,7 @@ - [Machine Learning FAQs on Quora](https://www.quora.com/topic/Machine-Learning/faq) -##Kaggle Competitions WriteUp +## Kaggle Competitions WriteUp - [How to almost win Kaggle Competitions](https://yanirseroussi.com/2014/08/24/how-to-almost-win-kaggle-competitions/) - [Convolution Neural Networks for EEG detection](http://blog.kaggle.com/2015/10/05/grasp-and-lift-eeg-detection-winners-interview-3rd-place-team-hedj/) - [Facebook Recruiting III Explained](http://alexminnaar.com/tag/kaggle-competitions.html) @@ -157,12 +157,14 @@ - [How to Rank 10% in Your First Kaggle Competition](https://dnc1994.com/2016/05/rank-10-percent-in-first-kaggle-competition-en/) -##Cheat Sheets -- [Probability Cheat Sheet](http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf), [Source](http://www.wzchen.com/probability-cheatsheet/) +## Cheat Sheets +- [Probability Cheat Sheet](http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf), +[Source](http://www.wzchen.com/probability-cheatsheet/) + - [Machine Learning Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet) -##Classification +## Classification - [Does Balancing Classes Improve Classifier Performance?](http://www.win-vector.com/blog/2015/02/does-balancing-classes-improve-classifier-performance/) - [What is Deviance?](http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart) - [When to choose which machine learning classifier?](http://stackoverflow.com/questions/2595176/when-to-choose-which-machine-learning-classifier) @@ -173,7 +175,7 @@ -##Linear Regression +## Linear Regression - [General](#general-) - [Assumptions of Linear Regression](http://pareonline.net/getvn.asp?n=2&v=8), [Stack Exchange](http://stats.stackexchange.com/questions/16381/what-is-a-complete-list-of-the-usual-assumptions-for-linear-regression) - [Linear Regression Comprehensive Resource](http://people.duke.edu/~rnau/regintro.htm) @@ -198,7 +200,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) -##Logistic Regression +## Logistic Regression - [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression) - [Geometric Intuition of Logistic Regression](http://florianhartl.com/logistic-regression-geometric-intuition.html) - [Obtaining predicted categories (choosing threshold)](http://stats.stackexchange.com/questions/25389/obtaining-predicted-values-y-1-or-0-from-a-logistic-regression-model-fit) @@ -208,7 +210,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Guide to an in-depth understanding of logistic regression](http://www.dataschool.io/guide-to-logistic-regression/) -##Model Validation using Resampling +## Model Validation using Resampling - [Resampling Explained](https://en.wikipedia.org/wiki/Resampling_(statistics)) - [Partioning data set in R](http://stackoverflow.com/questions/13536537/partitioning-data-set-in-r-based-on-multiple-classes-of-observations) @@ -243,7 +245,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. -##Deep Learning +## Deep Learning - [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning) - [Lots of Deep Learning Resources](http://deeplearning4j.org/documentation.html) - [Interesting Deep Learning and NLP Projects (Stanford)](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) @@ -300,7 +302,9 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Torch](http://torch.ch/) - [Torch ML Tutorial](http://code.madbits.com/wiki/doku.php), [Code](https://github.com/torch/tutorials) + - [Intro to Torch](http://ml.informatik.uni-freiburg.de/_media/teaching/ws1415/presentation_dl_lect3.pdf) + - [Learning Torch GitHub Repo](https://github.com/chetannaik/learning_torch) - [Awesome-Torch (Repository on GitHub)](https://github.com/carpedm20/awesome-torch) - [Machine Learning using Torch Oxford Univ](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/), [Code](https://github.com/oxford-cs-ml-2015) @@ -409,7 +413,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. -##Natural Language Processing +## Natural Language Processing - [A curated list of speech and natural language processing resources](https://github.com/edobashira/speech-language-processing) - [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) - [tf-idf explained](http://michaelerasm.us/tf-idf-in-10-minutes/) @@ -468,7 +472,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. -##Support Vector Machine +## Support Vector Machine - [Highest Voted Questions about SVMs on Cross Validated](http://stats.stackexchange.com/questions/tagged/svm) - [Help me Understand SVMs!](http://stats.stackexchange.com/questions/3947/help-me-understand-support-vector-machines) - [SVM in Layman's terms](https://www.quora.com/What-does-support-vector-machine-SVM-mean-in-laymans-terms) @@ -497,12 +501,12 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. -##Reinforcement Learning +## Reinforcement Learning - [Awesome Reinforcement Learning (GitHub)](https://github.com/aikorea/awesome-rl) - [RL Tutorial Part 1](http://outlace.com/Reinforcement-Learning-Part-1/), [Part 2](http://outlace.com/Reinforcement-Learning-Part-2/) -##Decision Trees +## Decision Trees - [Wikipedia Page - Lots of Good Info](https://en.wikipedia.org/wiki/Decision_tree_learning) - [FAQs about Decision Trees](http://stats.stackexchange.com/questions/tagged/cart) - [Brief Tour of Trees and Forests](http://statistical-research.com/a-brief-tour-of-the-trees-and-forests/) @@ -545,7 +549,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Probabilistic Trees Research Paper](http://people.stern.nyu.edu/adamodar/pdfiles/papers/probabilistic.pdf) -##Random Forest / Bagging +## Random Forest / Bagging - [Awesome Random Forest (GitHub)**](https://github.com/kjw0612/awesome-random-forest) - [How to tune RF parameters in practice?](https://www.kaggle.com/forums/f/15/kaggle-forum/t/4092/how-to-tune-rf-parameters-in-practice) - [Measures of variable importance in random forests](http://stats.stackexchange.com/questions/12605/measures-of-variable-importance-in-random-forests) @@ -559,7 +563,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Some Questions for R implementation](http://stackoverflow.com/questions/20537186/getting-predictions-after-rfimpute), [2](http://stats.stackexchange.com/questions/81609/whether-preprocessing-is-needed-before-prediction-using-finalmodel-of-randomfore), [3](http://stackoverflow.com/questions/17059432/random-forest-package-in-r-shows-error-during-prediction-if-there-are-new-fact) -##Boosting +## Boosting - [Boosting for Better Predictions](http://www.datasciencecentral.com/profiles/blogs/boosting-algorithms-for-better-predictions) - [Boosting Wikipedia Page](https://en.wikipedia.org/wiki/Boosting_(machine_learning)) - [Introduction to Boosted Trees | Tianqi Chen](https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf) @@ -584,7 +588,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Tutorial](http://math.mit.edu/~rothvoss/18.304.3PM/Presentations/1-Eric-Boosting304FinalRpdf.pdf) -##Ensembles +## Ensembles - [Wikipedia Article on Ensemble Learning](https://en.wikipedia.org/wiki/Ensemble_learning) - [Kaggle Ensembling Guide](http://mlwave.com/kaggle-ensembling-guide/) - [The Power of Simple Ensembles](http://www.overkillanalytics.net/more-is-always-better-the-power-of-simple-ensembles/) @@ -599,14 +603,14 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [How are classifications merged in an ensemble classifier?](http://stats.stackexchange.com/questions/21502/how-are-classifications-merged-in-an-ensemble-classifier) -##Stacking Models +## Stacking Models - [Stacking, Blending and Stacked Generalization](http://www.chioka.in/stacking-blending-and-stacked-generalization/) - [Stacked Generalization (Stacking)](http://machine-learning.martinsewell.com/ensembles/stacking/) - [Stacked Generalization: when does it work?](http://www.ijcai.org/Proceedings/97-2/011.pdf) - [Stacked Generalization Paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.1533&rep=rep1&type=pdf) -##Vapnik–Chervonenkis Dimension +## Vapnik–Chervonenkis Dimension - [Wikipedia article on VC Dimension](https://en.wikipedia.org/wiki/VC_dimension) - [Intuitive Explanantion of VC Dimension](https://www.quora.com/Explain-VC-dimension-and-shattering-in-lucid-Way) - [Video explaining VC Dimension](https://www.youtube.com/watch?v=puDzy2XmR5c) @@ -616,7 +620,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. -##Bayesian Machine Learning +## Bayesian Machine Learning - [Bayesian Methods for Hackers (using pyMC)](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - [Should all Machine Learning be Bayesian?](http://videolectures.net/bark08_ghahramani_samlbb/) - [Tutorial on Bayesian Optimisation for Machine Learning](http://www.iro.umontreal.ca/~bengioy/cifar/NCAP2014-summerschool/slides/Ryan_adams_140814_bayesopt_ncap.pdf) @@ -627,7 +631,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. -##Semi Supervised Learning +## Semi Supervised Learning - [Wikipedia article on Semi Supervised Learning](https://en.wikipedia.org/wiki/Semi-supervised_learning) - [Tutorial on Semi Supervised Learning](http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf) - [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) @@ -639,7 +643,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. -##Optimization +## Optimization - [Mean Variance Portfolio Optimization with R and Quadratic Programming](http://www.wdiam.com/2012/06/10/mean-variance-portfolio-optimization-with-r-and-quadratic-programming/?utm_content=buffer04c12&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer) - [Algorithms for Sparse Optimization and Machine Learning](http://www.ima.umn.edu/2011-2012/W3.26-30.12/activities/Wright-Steve/sjw-ima12) @@ -650,6 +654,6 @@ Learning](http://www.ima.umn.edu/2011-2012/W3.26-30.12/activities/Wright-Steve/s - [The Interplay of Optimization and Machine Learning Research](http://jmlr.org/papers/volume7/MLOPT-intro06a/MLOPT-intro06a.pdf) -##Other Tutorials +## Other Tutorials - For a collection of Data Science Tutorials using R, please refer to [this list](https://github.com/ujjwalkarn/DataScienceR). - For a collection of Data Science Tutorials using Python, please refer to [this list](https://github.com/ujjwalkarn/DataSciencePython). From 001338e6bddbc7f30236ee85f768cb4aa77aa261 Mon Sep 17 00:00:00 2001 From: Manash Kumar Mandal Date: Fri, 24 Mar 2017 11:46:59 +0600 Subject: [PATCH 41/77] Update README.md --- README.md | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/README.md b/README.md index 5e0af1f..55faee1 100644 --- a/README.md +++ b/README.md @@ -52,24 +52,43 @@ ## Miscellaneous - [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) + - [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning) + - [A curated list of awesome Machine Learning frameworks, libraries and software](https://github.com/josephmisiti/awesome-machine-learning) + - [A curated list of awesome data visualization libraries and resources.](https://github.com/fasouto/awesome-dataviz) + - [An awesome Data Science repository to learn and apply for real world problems](https://github.com/okulbilisim/awesome-datascience) + - [The Open Source Data Science Masters](http://datasciencemasters.org/) + - [Machine Learning FAQs on Cross Validated](http://stats.stackexchange.com/questions/tagged/machine-learning) + - [List of Machine Learning University Courses](https://github.com/prakhar1989/awesome-courses#machine-learning) + - [Machine Learning algorithms that you should always have a strong understanding of](https://www.quora.com/What-are-some-Machine-Learning-algorithms-that-you-should-always-have-a-strong-understanding-of-and-why) + - [Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables](http://terpconnect.umd.edu/~bmomen/BIOM621/LineardepCorrOrthogonal.pdf) + - [List of Machine Learning Concepts](https://en.wikipedia.org/wiki/List_of_machine_learning_concepts) + - [Slides on Several Machine Learning Topics](http://www.slideshare.net/pierluca.lanzi/presentations) + - [MIT Machine Learning Lecture Slides](http://www.ai.mit.edu/courses/6.867-f04/lectures.html) + - [Comparison Supervised Learning Algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/) + - [Learning Data Science Fundamentals](http://www.dataschool.io/learning-data-science-fundamentals/) + - [Machine Learning mistakes to avoid](https://medium.com/@nomadic_mind/new-to-machine-learning-avoid-these-three-mistakes-73258b3848a4#.lih061l3l) + - [Statistical Machine Learning Course](http://www.stat.cmu.edu/~larry/=sml/) + - [TheAnalyticsEdge edX Notes and Codes](https://github.com/pedrosan/TheAnalyticsEdge) + - [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) + - [Have Fun With Machine Learning](https://github.com/humphd/have-fun-with-machine-learning) From c78d10afd3fcb011721ec966b58c1ff5703743c1 Mon Sep 17 00:00:00 2001 From: Manash Kumar Mandal Date: Fri, 24 Mar 2017 11:48:06 +0600 Subject: [PATCH 42/77] Update README.md --- README.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/README.md b/README.md index 55faee1..99f0c57 100644 --- a/README.md +++ b/README.md @@ -51,6 +51,7 @@ ## Miscellaneous + - [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) - [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning) @@ -92,30 +93,50 @@ - [Have Fun With Machine Learning](https://github.com/humphd/have-fun-with-machine-learning) + ## Interview Resources + - [41 Essential Machine Learning Interview Questions (with answers)](https://www.springboard.com/blog/machine-learning-interview-questions/) + - [How can a computer science graduate student prepare himself for data scientist interviews?](https://www.quora.com/How-can-a-computer-science-graduate-student-prepare-himself-for-data-scientist-machine-learning-intern-interviews) + - [How do I learn Machine Learning?](https://www.quora.com/How-do-I-learn-machine-learning-1) + - [FAQs about Data Science Interviews](https://www.quora.com/topic/Data-Science-Interviews/faq) + - [What are the key skills of a data scientist?](https://www.quora.com/What-are-the-key-skills-of-a-data-scientist) ## Artificial Intelligence + - [Awesome Artificial Intelligence (GitHub Repo)](https://github.com/owainlewis/awesome-artificial-intelligence) + - [UC Berkeley CS188 Intro to AI](http://ai.berkeley.edu/home.html), [Lecture Videos](http://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM) + - [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/) + - [edX course | Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) + - [Udacity Course | Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) + - [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) + ## Genetic Algorithms + - [Genetic Algorithms Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm) + - [Simple Implementation of Genetic Algorithms in Python (Part 1)](http://outlace.com/Simple-Genetic-Algorithm-in-15-lines-of-Python/), [Part 2](http://outlace.com/Simple-Genetic-Algorithm-Python-Addendum/) + - [Genetic Algorithms vs Artificial Neural Networks](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks) + - [Genetic Algorithms Explained in Plain English](http://www.ai-junkie.com/ga/intro/gat1.html) + - [Genetic Programming](https://en.wikipedia.org/wiki/Genetic_programming) + - [Genetic Programming in Python (GitHub)](https://github.com/trevorstephens/gplearn) + - [Genetic Alogorithms vs Genetic Programming (Quora)](https://www.quora.com/Whats-the-difference-between-Genetic-Algorithms-and-Genetic-Programming), [StackOverflow](http://stackoverflow.com/questions/3819977/what-are-the-differences-between-genetic-algorithms-and-genetic-programming) From d148d93db854c670b5fcd7699c5fcc982e676581 Mon Sep 17 00:00:00 2001 From: Manash Kumar Mandal Date: Fri, 24 Mar 2017 12:01:03 +0600 Subject: [PATCH 43/77] fix broken formatting --- README.md | 450 +++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 442 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 99f0c57..dcaed24 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ - [Curated list of Python tutorials for Data Science, NLP and Machine Learning](https://github.com/ujjwalkarn/DataSciencePython). -##Table of Contents +## Table of Contents - [Miscellaneous](#general) - [Interview Resources](#interview) - [Artificial Intelligence](#ai) @@ -50,6 +50,7 @@ - [Other Useful Tutorials](#other) + ## Miscellaneous - [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) @@ -107,6 +108,7 @@ - [What are the key skills of a data scientist?](https://www.quora.com/What-are-the-key-skills-of-a-data-scientist) + ## Artificial Intelligence - [Awesome Artificial Intelligence (GitHub Repo)](https://github.com/owainlewis/awesome-artificial-intelligence) @@ -140,99 +142,172 @@ - [Genetic Alogorithms vs Genetic Programming (Quora)](https://www.quora.com/Whats-the-difference-between-Genetic-Algorithms-and-Genetic-Programming), [StackOverflow](http://stackoverflow.com/questions/3819977/what-are-the-differences-between-genetic-algorithms-and-genetic-programming) + ## Statistics + - [Stat Trek Website](http://stattrek.com/) - A dedicated website to teach yourselves Statistics + - [Learn Statistics Using Python](https://github.com/rouseguy/intro2stats) - Learn Statistics using an application-centric programming approach + - [Statistics for Hackers | Slides | @jakevdp](https://speakerdeck.com/jakevdp/statistics-for-hackers) - Slides by Jake VanderPlas + - [Online Statistics Book](http://onlinestatbook.com/2/index.html) - An Interactive Multimedia Course for Studying Statistics + - [What is a Sampling Distribution?](http://stattrek.com/sampling/sampling-distribution.aspx) + - Tutorials + - [AP Statistics Tutorial](http://stattrek.com/tutorials/ap-statistics-tutorial.aspx) + - [Statistics and Probability Tutorial](http://stattrek.com/tutorials/statistics-tutorial.aspx) + - [Matrix Algebra Tutorial](http://stattrek.com/tutorials/matrix-algebra-tutorial.aspx) + - [What is an Unbiased Estimator?](https://www.physicsforums.com/threads/what-is-an-unbiased-estimator.547728/) + - [Goodness of Fit Explained](https://en.wikipedia.org/wiki/Goodness_of_fit) + - [What are QQ Plots?](http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html) + - [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os) - Free PDF textbook + ## Useful Blogs + - [Edwin Chen's Blog](http://blog.echen.me/) - A blog about Math, stats, ML, crowdsourcing, data science + - [The Data School Blog](http://www.dataschool.io/) - Data science for beginners! + - [ML Wave](http://mlwave.com/) - A blog for Learning Machine Learning + - [Andrej Karpathy](http://karpathy.github.io/) - A blog about Deep Learning and Data Science in general + - [Colah's Blog](http://colah.github.io/) - Awesome Neural Networks Blog + - [Alex Minnaar's Blog](http://alexminnaar.com/) - A blog about Machine Learning and Software Engineering + - [Statistically Significant](http://andland.github.io/) - Andrew Landgraf's Data Science Blog + - [Simply Statistics](http://simplystatistics.org/) - A blog by three biostatistics professors + - [Yanir Seroussi's Blog](https://yanirseroussi.com/) - A blog about Data Science and beyond + - [fastML](http://fastml.com/) - Machine learning made easy + - [Trevor Stephens Blog](http://trevorstephens.com/) - Trevor Stephens Personal Page + - [no free hunch | kaggle](http://blog.kaggle.com/) - The Kaggle Blog about all things Data Science + - [A Quantitative Journey | outlace](http://outlace.com/) - learning quantitative applications + - [r4stats](http://r4stats.com/) - analyze the world of data science, and to help people learn to use R + - [Variance Explained](http://varianceexplained.org/) - David Robinson's Blog + - [AI Junkie](http://www.ai-junkie.com/) - a blog about Artificial Intellingence + - [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/)- Making deep learning accessible + - [J Alammar's Blog](http://jalammar.github.io/)- Blog posts about Machine Learning and Neural Nets + - [Adam Geitgey](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.f7vwrtfne) - Easiest Introduction to machine learning + ## Resources on Quora + - [Most Viewed Machine Learning writers](https://www.quora.com/topic/Machine-Learning/writers) + - [Data Science Topic on Quora](https://www.quora.com/Data-Science) + - [William Chen's Answers](https://www.quora.com/William-Chen-6/answers) + - [Michael Hochster's Answers](https://www.quora.com/Michael-Hochster/answers) + - [Ricardo Vladimiro's Answers](https://www.quora.com/Ricardo-Vladimiro-1/answers) + - [Storytelling with Statistics](https://datastories.quora.com/) + - [Data Science FAQs on Quora](https://www.quora.com/topic/Data-Science/faq) + - [Machine Learning FAQs on Quora](https://www.quora.com/topic/Machine-Learning/faq) + ## Kaggle Competitions WriteUp + - [How to almost win Kaggle Competitions](https://yanirseroussi.com/2014/08/24/how-to-almost-win-kaggle-competitions/) + - [Convolution Neural Networks for EEG detection](http://blog.kaggle.com/2015/10/05/grasp-and-lift-eeg-detection-winners-interview-3rd-place-team-hedj/) + - [Facebook Recruiting III Explained](http://alexminnaar.com/tag/kaggle-competitions.html) + - [Predicting CTR with Online ML](http://mlwave.com/predicting-click-through-rates-with-online-machine-learning/) + - [How to Rank 10% in Your First Kaggle Competition](https://dnc1994.com/2016/05/rank-10-percent-in-first-kaggle-competition-en/) + ## Cheat Sheets + - [Probability Cheat Sheet](http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf), [Source](http://www.wzchen.com/probability-cheatsheet/) - [Machine Learning Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet) + ## Classification + - [Does Balancing Classes Improve Classifier Performance?](http://www.win-vector.com/blog/2015/02/does-balancing-classes-improve-classifier-performance/) + - [What is Deviance?](http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart) + - [When to choose which machine learning classifier?](http://stackoverflow.com/questions/2595176/when-to-choose-which-machine-learning-classifier) + - [What are the advantages of different classification algorithms?](https://www.quora.com/What-are-the-advantages-of-different-classification-algorithms) + - [ROC and AUC Explained](http://www.dataschool.io/roc-curves-and-auc-explained/) ([related video](https://youtu.be/OAl6eAyP-yo)) + - [An introduction to ROC analysis](https://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf) + - [Simple guide to confusion matrix terminology](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) + ## Linear Regression + - [General](#general-) + - [Assumptions of Linear Regression](http://pareonline.net/getvn.asp?n=2&v=8), [Stack Exchange](http://stats.stackexchange.com/questions/16381/what-is-a-complete-list-of-the-usual-assumptions-for-linear-regression) + - [Linear Regression Comprehensive Resource](http://people.duke.edu/~rnau/regintro.htm) + - [Applying and Interpreting Linear Regression](http://www.dataschool.io/applying-and-interpreting-linear-regression/) + - [What does having constant variance in a linear regression model mean?](http://stats.stackexchange.com/questions/52089/what-does-having-constant-variance-in-a-linear-regression-model-mean/52107?stw=2#52107) + - [Difference between linear regression on y with x and x with y](http://stats.stackexchange.com/questions/22718/what-is-the-difference-between-linear-regression-on-y-with-x-and-x-with-y?lq=1) + - [Is linear regression valid when the dependant variable is not normally distributed?](https://www.researchgate.net/post/Is_linear_regression_valid_when_the_outcome_dependant_variable_not_normally_distributed) - Multicollinearity and VIF + - [Dummy Variable Trap | Multicollinearity](https://en.wikipedia.org/wiki/Multicollinearity) + - [Dealing with multicollinearity using VIFs](https://jonlefcheck.net/2012/12/28/dealing-with-multicollinearity-using-variance-inflation-factors/) - [Residual Analysis](#residuals-) + - [Interpreting plot.lm() in R](http://stats.stackexchange.com/questions/58141/interpreting-plot-lm) + - [How to interpret a QQ plot?](http://stats.stackexchange.com/questions/101274/how-to-interpret-a-qq-plot?lq=1) + - [Interpreting Residuals vs Fitted Plot](http://stats.stackexchange.com/questions/76226/interpreting-the-residuals-vs-fitted-values-plot-for-verifying-the-assumptions) - [Outliers](#outliers-) + - [How should outliers be dealt with?](http://stats.stackexchange.com/questions/175/how-should-outliers-be-dealt-with-in-linear-regression-analysis) - [Elastic Net](https://en.wikipedia.org/wiki/Elastic_net_regularization) @@ -240,116 +315,200 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) + ## Logistic Regression + - [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression) + - [Geometric Intuition of Logistic Regression](http://florianhartl.com/logistic-regression-geometric-intuition.html) + - [Obtaining predicted categories (choosing threshold)](http://stats.stackexchange.com/questions/25389/obtaining-predicted-values-y-1-or-0-from-a-logistic-regression-model-fit) + - [Residuals in logistic regression](http://stats.stackexchange.com/questions/1432/what-do-the-residuals-in-a-logistic-regression-mean) + - [Difference between logit and probit models](http://stats.stackexchange.com/questions/20523/difference-between-logit-and-probit-models#30909), [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression), [Probit Model Wiki](https://en.wikipedia.org/wiki/Probit_model) + - [Pseudo R2 for Logistic Regression](http://stats.stackexchange.com/questions/3559/which-pseudo-r2-measure-is-the-one-to-report-for-logistic-regression-cox-s), [How to calculate](http://stats.stackexchange.com/questions/8511/how-to-calculate-pseudo-r2-from-rs-logistic-regression), [Other Details](http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm) + - [Guide to an in-depth understanding of logistic regression](http://www.dataschool.io/guide-to-logistic-regression/) + ## Model Validation using Resampling - [Resampling Explained](https://en.wikipedia.org/wiki/Resampling_(statistics)) + - [Partioning data set in R](http://stackoverflow.com/questions/13536537/partitioning-data-set-in-r-based-on-multiple-classes-of-observations) + - [Implementing hold-out Validaion in R](http://stackoverflow.com/questions/22972854/how-to-implement-a-hold-out-validation-in-r), [2](http://www.gettinggeneticsdone.com/2011/02/split-data-frame-into-testing-and.html) + - [Cross Validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) + - [Training with Full dataset after CV?](http://stats.stackexchange.com/questions/11602/training-with-the-full-dataset-after-cross-validation) + - [Which CV method is best?](http://stats.stackexchange.com/questions/103459/how-do-i-know-which-method-of-cross-validation-is-best) + - [Variance Estimates in k-fold CV](http://stats.stackexchange.com/questions/31190/variance-estimates-in-k-fold-cross-validation) + - [Is CV a subsitute for Validation Set?](http://stats.stackexchange.com/questions/18856/is-cross-validation-a-proper-substitute-for-validation-set) + - [Choice of k in k-fold CV](http://stats.stackexchange.com/questions/27730/choice-of-k-in-k-fold-cross-validation) + - [CV for ensemble learning](http://stats.stackexchange.com/questions/102631/k-fold-cross-validation-of-ensemble-learning) + - [k-fold CV in R](http://stackoverflow.com/questions/22909197/creating-folds-for-k-fold-cv-in-r-using-caret) + - [Good Resources](http://www.chioka.in/tag/cross-validation/) + - Overfitting and Cross Validation + - [Preventing Overfitting the Cross Validation Data | Andrew Ng](http://ai.stanford.edu/~ang/papers/cv-final.pdf) - - [Over-fitting in Model Selection and Subsequent Selection Bias in -Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a.pdf) + + - [Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a.pdf) + - [CV for detecting and preventing Overfitting](http://www.autonlab.org/tutorials/overfit10.pdf) + - [How does CV overcome the Overfitting Problem](http://stats.stackexchange.com/questions/9053/how-does-cross-validation-overcome-the-overfitting-problem) + - - [Bootstrapping](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)) + - [Why Bootstrapping Works?](http://stats.stackexchange.com/questions/26088/explaining-to-laypeople-why-bootstrapping-works) + - [Good Animation](https://www.stat.auckland.ac.nz/~wild/BootAnim/) + - [Example of Bootstapping](http://statistics.about.com/od/Applications/a/Example-Of-Bootstrapping.htm) + - [Understanding Bootstapping for Validation and Model Selection](http://stats.stackexchange.com/questions/14516/understanding-bootstrapping-for-validation-and-model-selection?rq=1) + - [Cross Validation vs Bootstrap to estimate prediction error](http://stats.stackexchange.com/questions/18348/differences-between-cross-validation-and-bootstrapping-to-estimate-the-predictio), [Cross-validation vs .632 bootstrapping to evaluate classification performance](http://stats.stackexchange.com/questions/71184/cross-validation-or-bootstrapping-to-evaluate-classification-performance) + ## Deep Learning + - [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning) + - [Lots of Deep Learning Resources](http://deeplearning4j.org/documentation.html) + - [Interesting Deep Learning and NLP Projects (Stanford)](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) + - [Core Concepts of Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) + - [Understanding Natural Language with Deep Neural Networks Using Torch](https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) + - [Stanford Deep Learning Tutorial](http://ufldl.stanford.edu/tutorial/) + - [Deep Learning FAQs on Quora](https://www.quora.com/topic/Deep-Learning/faq) + - [Google+ Deep Learning Page](https://plus.google.com/communities/112866381580457264725) + - [Recent Reddit AMAs related to Deep Learning](http://deeplearning.net/2014/11/22/recent-reddit-amas-about-deep-learning/), [Another AMA](https://www.reddit.com/r/IAmA/comments/3mdk9v/we_are_google_researchers_working_on_deep/) + - [Where to Learn Deep Learning?](http://www.kdnuggets.com/2014/05/learn-deep-learning-courses-tutorials-overviews.html) + - [Deep Learning nvidia concepts](http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) + - [Introduction to Deep Learning Using Python (GitHub)](https://github.com/rouseguy/intro2deeplearning), [Good Introduction Slides](https://speakerdeck.com/bargava/introduction-to-deep-learning) + - [Video Lectures Oxford 2015](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu), [Video Lectures Summer School Montreal](http://videolectures.net/deeplearning2015_montreal/) + - [Deep Learning Software List](http://deeplearning.net/software_links/) + - [Hacker's guide to Neural Nets](http://karpathy.github.io/neuralnets/) + - [Top arxiv Deep Learning Papers explained](http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html) + - [Geoff Hinton Youtube Vidoes on Deep Learning](https://www.youtube.com/watch?v=IcOMKXAw5VA) + - [Awesome Deep Learning Reading List](http://deeplearning.net/reading-list/) + - [Deep Learning Comprehensive Website](http://deeplearning.net/), [Software](http://deeplearning.net/software_links/) + - [deeplearning Tutorials](http://deeplearning4j.org/) + - [AWESOME! Deep Learning Tutorial](https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) + - [Deep Learning Basics](http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html) + - [Stanford Tutorials](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/) + - [Train, Validation & Test in Artificial Neural Networks](http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ) + - [Artificial Neural Networks Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks) + - [Neural Networks FAQs on Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50) + - [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html) + - [Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/) - Neural Machine Translation + - [Introduction to Neural Machine Translation with GPUs (part 1)](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) + - [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/) + - Deep Learning Frameworks + - [Torch vs. Theano](http://fastml.com/torch-vs-theano/) + - [dl4j vs. torch7 vs. theano](http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html) + - [Deep Learning Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/) + - [Theano](https://en.wikipedia.org/wiki/Theano_(software)) + - [Website](http://deeplearning.net/software/theano/) + - [Theano Introduction](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/) + - [Theano Tutorial](http://outlace.com/Beginner-Tutorial-Theano/) + - [Good Theano Tutorial](http://deeplearning.net/software/theano/tutorial/) + - [Logistic Regression using Theano for classifying digits](http://deeplearning.net/tutorial/logreg.html#logreg) + - [MLP using Theano](http://deeplearning.net/tutorial/mlp.html#mlp) + - [CNN using Theano](http://deeplearning.net/tutorial/lenet.html#lenet) + - [RNNs using Theano](http://deeplearning.net/tutorial/rnnslu.html#rnnslu) + - [LSTM for Sentiment Analysis in Theano](http://deeplearning.net/tutorial/lstm.html#lstm) + - [RBM using Theano](http://deeplearning.net/tutorial/rbm.html#rbm) + - [DBNs using Theano](http://deeplearning.net/tutorial/DBN.html#dbn) + - [All Codes](https://github.com/lisa-lab/DeepLearningTutorials) + - [Deep Learning Implementation Tutorials - Keras and Lasagne](https://github.com/vict0rsch/deep_learning/) - [Torch](http://torch.ch/) + - [Torch ML Tutorial](http://code.madbits.com/wiki/doku.php), [Code](https://github.com/torch/tutorials) - [Intro to Torch](http://ml.informatik.uni-freiburg.de/_media/teaching/ws1415/presentation_dl_lect3.pdf) - [Learning Torch GitHub Repo](https://github.com/chetannaik/learning_torch) + - [Awesome-Torch (Repository on GitHub)](https://github.com/carpedm20/awesome-torch) + - [Machine Learning using Torch Oxford Univ](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/), [Code](https://github.com/oxford-cs-ml-2015) + - [Torch Internals Overview](https://apaszke.github.io/torch-internals.html) + - [Torch Cheatsheet](https://github.com/torch/torch7/wiki/Cheatsheet) + - [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) - Caffe @@ -357,343 +516,618 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - TensorFlow - [Website](http://tensorflow.org/) + - [TensorFlow Examples for Beginners](https://github.com/aymericdamien/TensorFlow-Examples) + - [Stanford Tensorflow for Deep Learning Research Course](https://web.stanford.edu/class/cs20si/syllabus.html) + - [GitHub Repo](https://github.com/chiphuyen/tf-stanford-tutorials) + - [Simplified Scikit-learn Style Interface to TensorFlow](https://github.com/tensorflow/skflow) + - [Learning TensorFlow GitHub Repo](https://github.com/chetannaik/learning_tensorflow) + - [Benchmark TensorFlow GitHub](https://github.com/soumith/convnet-benchmarks/issues/66) + - [Awesome TensorFlow List](https://github.com/jtoy/awesome-tensorflow) + - [TensorFlow Book](https://github.com/BinRoot/TensorFlow-Book) + - [Android TensorFlow Machine Learning Example](https://blog.mindorks.com/android-tensorflow-machine-learning-example-ff0e9b2654cc) + - [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMachineLearningExample) + - Feed Forward Networks + - [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/) + - [Implementing a Neural Network from scratch](http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/), [Code](https://github.com/dennybritz/nn-from-scratch) + - [Speeding up your Neural Network with Theano and the gpu](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/), [Code](https://github.com/dennybritz/nn-theano) + - [Basic ANN Theory](https://takinginitiative.wordpress.com/2008/04/03/basic-neural-network-tutorial-theory/) + - [Role of Bias in Neural Networks](http://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks) + - [Choosing number of hidden layers and nodes](http://stackoverflow.com/questions/3345079/estimating-the-number-of-neurons-and-number-of-layers-of-an-artificial-neural-ne),[2](http://stackoverflow.com/questions/10565868/multi-layer-perceptron-mlp-architecture-criteria-for-choosing-number-of-hidde?lq=1),[3](http://stackoverflow.com/questions/9436209/how-to-choose-number-of-hidden-layers-and-nodes-in-neural-network/2#) + - [Backpropagation in Matrix Form](http://sudeepraja.github.io/Neural/) + - [ANN implemented in C++ | AI Junkie](http://www.ai-junkie.com/ann/evolved/nnt6.html) + - [Simple Implementation](http://stackoverflow.com/questions/15395835/simple-multi-layer-neural-network-implementation) + - [NN for Beginners](http://www.codeproject.com/Articles/16419/AI-Neural-Network-for-beginners-Part-of) + - [Regression and Classification with NNs (Slides)](http://www.autonlab.org/tutorials/neural13.pdf) + - [Another Intro](http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html) + - Recurrent and LSTM Networks - [awesome-rnn: list of resources (GitHub Repo)](https://github.com/kjw0612/awesome-rnn) + - [Recurrent Neural Net Tutorial Part 1](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/), [Part 2] (http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/), [Part 3] (http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/), [Code](https://github.com/dennybritz/rnn-tutorial-rnnlm/) + - [NLP RNN Representations](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/) + - [The Unreasonable effectiveness of RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/), [Torch Code](https://github.com/karpathy/char-rnn), [Python Code](https://gist.github.com/karpathy/d4dee566867f8291f086) + - [Intro to RNN](http://deeplearning4j.org/recurrentnetwork.html), [LSTM](http://deeplearning4j.org/lstm.html) + - [An application of RNN](http://hackaday.com/2015/10/15/73-computer-scientists-created-a-neural-net-and-you-wont-believe-what-happened-next/) + - [Optimizing RNN Performance](http://svail.github.io/) + - [Simple RNN](http://outlace.com/Simple-Recurrent-Neural-Network/) + - [Auto-Generating Clickbait with RNN](https://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/) + - [Sequence Learning using RNN (Slides)](http://www.slideshare.net/indicods/general-sequence-learning-with-recurrent-neural-networks-for-next-ml) + - [Machine Translation using RNN (Paper)](http://emnlp2014.org/papers/pdf/EMNLP2014179.pdf) + - [Music generation using RNNs (Keras)](https://github.com/MattVitelli/GRUV) + - [Using RNN to create on-the-fly dialogue (Keras)](http://neuralniche.com/post/tutorial/) + - Long Short Term Memory (LSTM) + - [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) + - [LSTM explained](https://apaszke.github.io/lstm-explained.html) + - [Beginner’s Guide to LSTM](http://deeplearning4j.org/lstm.html) + - [Implementing LSTM from scratch](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/), [Python/Theano code](https://github.com/dennybritz/rnn-tutorial-gru-lstm) + - [Torch Code for character-level language models using LSTM](https://github.com/karpathy/char-rnn) + - [LSTM for Kaggle EEG Detection competition (Torch Code)](https://github.com/apaszke/kaggle-grasp-and-lift) + - [LSTM for Sentiment Analysis in Theano](http://deeplearning.net/tutorial/lstm.html#lstm) + - [Deep Learning for Visual Q&A | LSTM | CNN](http://avisingh599.github.io/deeplearning/visual-qa/), [Code](https://github.com/avisingh599/visual-qa) + - [Computer Responds to email using LSTM | Google](http://googleresearch.blogspot.in/2015/11/computer-respond-to-this-email.html) + - [LSTM dramatically improves Google Voice Search](http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html), [Another Article](http://deeplearning.net/2015/09/30/long-short-term-memory-dramatically-improves-google-voice-etc-now-available-to-a-billion-users/) + - [Understanding Natural Language with LSTM Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) + - [Torch code for Visual Question Answering using a CNN+LSTM model](https://github.com/abhshkdz/neural-vqa) + - Gated Recurrent Units (GRU) + - [LSTM vs GRU](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/) + - [Recursive Neural Network (not Recurrent)](https://en.wikipedia.org/wiki/Recursive_neural_network) + - [Recursive Neural Tensor Network (RNTN)](http://deeplearning4j.org/recursiveneuraltensornetwork.html) + - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) + - Restricted Boltzmann Machine + - [Beginner's Guide about RBMs](http://deeplearning4j.org/restrictedboltzmannmachine.html) + - [Another Good Tutorial](http://deeplearning.net/tutorial/rbm.html) + - [Introduction to RBMs](http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/) + - [Hinton's Guide to Training RBMs](https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf) + - [RBMs in R](https://github.com/zachmayer/rbm) + - [Deep Belief Networks Tutorial](http://deeplearning4j.org/deepbeliefnetwork.html) + - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) + - Autoencoders: Unsupervised (applies BackProp after setting target = input) + - [Andrew Ng Sparse Autoencoders pdf](https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf) + - [Deep Autoencoders Tutorial](http://deeplearning4j.org/deepautoencoder.html) + - [Denoising Autoencoders](http://deeplearning.net/tutorial/dA.html), [Theano Code](http://deeplearning.net/tutorial/code/dA.py) + - [Stacked Denoising Autoencoders](http://deeplearning.net/tutorial/SdA.html#sda) + - Convolutional Neural Networks + - [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) + - [Awesome Deep Vision: List of Resources (GitHub)](https://github.com/kjw0612/awesome-deep-vision) + - [Intro to CNNs](http://deeplearning4j.org/convolutionalnets.html) + - [Understanding CNN for NLP](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/) + - [Stanford Notes](http://vision.stanford.edu/teaching/cs231n/), [Codes](http://cs231n.github.io/), [GitHub](https://github.com/cs231n/cs231n.github.io) + - [JavaScript Library (Browser Based) for CNNs](http://cs.stanford.edu/people/karpathy/convnetjs/) + - [Using CNNs to detect facial keypoints](http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/) + - [Deep learning to classify business photos at Yelp](http://engineeringblog.yelp.com/2015/10/how-we-use-deep-learning-to-classify-business-photos-at-yelp.html) + - [Interview with Yann LeCun | Kaggle](http://blog.kaggle.com/2014/12/22/convolutional-nets-and-cifar-10-an-interview-with-yan-lecun/) + - [Visualising and Understanding CNNs](https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf) + ## Natural Language Processing + - [A curated list of speech and natural language processing resources](https://github.com/edobashira/speech-language-processing) + - [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) + - [tf-idf explained](http://michaelerasm.us/tf-idf-in-10-minutes/) + - [Interesting Deep Learning NLP Projects Stanford](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) + - [NLP from Scratch | Google Paper](https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/35671.pdf) + - [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) + - [Bag of Words](https://en.wikipedia.org/wiki/Bag-of-words_model) + - [Classification text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) + + - [Topic Modeling](https://en.wikipedia.org/wiki/Topic_model) + - [LDA](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation), [LSA](https://en.wikipedia.org/wiki/Latent_semantic_analysis), [Probabilistic LSA](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) + - [What is a good explanation of Latent Dirichlet Allocation?](https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation) + - [Awesome LDA Explanation!](http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/). [Another good explanation](http://confusedlanguagetech.blogspot.in/2012/07/jordan-boyd-graber-and-philip-resnik.html) + - [The LDA Buffet- Intuitive Explanation](http://www.matthewjockers.net/2011/09/29/the-lda-buffet-is-now-open-or-latent-dirichlet-allocation-for-english-majors/) + - [Difference between LSI and LDA](https://www.quora.com/Whats-the-difference-between-Latent-Semantic-Indexing-LSI-and-Latent-Dirichlet-Allocation-LDA) + - [Original LDA Paper](https://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf) + - [alpha and beta in LDA](http://datascience.stackexchange.com/questions/199/what-does-the-alpha-and-beta-hyperparameters-contribute-to-in-latent-dirichlet-a) + - [Intuitive explanation of the Dirichlet distribution](https://www.quora.com/What-is-an-intuitive-explanation-of-the-Dirichlet-distribution) + - [Topic modeling made just simple enough](https://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/) + - [Online LDA](http://alexminnaar.com/online-latent-dirichlet-allocation-the-best-option-for-topic-modeling-with-large-data-sets.html), [Online LDA with Spark](http://alexminnaar.com/distributed-online-latent-dirichlet-allocation-with-apache-spark.html) + - [LDA in Scala](http://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-i-the-theory.html), [Part 2](http://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-ii-the-code.html) + - [Segmentation of Twitter Timelines via Topic Modeling](http://alexperrier.github.io/jekyll/update/2015/09/16/segmentation_twitter_timelines_lda_vs_lsa.html) + - [Topic Modeling of Twitter Followers](http://alexperrier.github.io/jekyll/update/2015/09/04/topic-modeling-of-twitter-followers.html) + - word2vec + - [Google word2vec](https://code.google.com/archive/p/word2vec) + - [Bag of Words Model Wiki](https://en.wikipedia.org/wiki/Bag-of-words_model) + - [word2vec Tutorial](https://rare-technologies.com/word2vec-tutorial/) + - [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) + - [Skip Gram Model Tutorial](http://alexminnaar.com/word2vec-tutorial-part-i-the-skip-gram-model.html), [CBoW Model](http://alexminnaar.com/word2vec-tutorial-part-ii-the-continuous-bag-of-words-model.html) + - [Word Vectors Kaggle Tutorial Python](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors), [Part 2](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-3-more-fun-with-word-vectors) + - [Making sense of word2vec](http://rare-technologies.com/making-sense-of-word2vec/) + - [word2vec explained on deeplearning4j](http://deeplearning4j.org/word2vec.html) + - [Quora word2vec](https://www.quora.com/How-does-word2vec-work) + - [Other Quora Resources](https://www.quora.com/What-are-the-continuous-bag-of-words-and-skip-gram-architectures-in-laymans-terms), [2](https://www.quora.com/What-is-the-difference-between-the-Bag-of-Words-model-and-the-Continuous-Bag-of-Words-model), [3](https://www.quora.com/Is-skip-gram-negative-sampling-better-than-CBOW-NS-for-word2vec-If-so-why) + - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) - Text Clustering + - [How string clustering works](http://stackoverflow.com/questions/8196371/how-clustering-works-especially-string-clustering) + - [Levenshtein distance for measuring the difference between two sequences](https://en.wikipedia.org/wiki/Levenshtein_distance) + - [Text clustering with Levenshtein distances](http://stackoverflow.com/questions/21511801/text-clustering-with-levenshtein-distances) - Text Classification + - [Classification Text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) - [Language learning with NLP and reinforcement learning](http://blog.dennybritz.com/2015/09/11/reimagining-language-learning-with-nlp-and-reinforcement-learning/) + - [Kaggle Tutorial Bag of Words and Word vectors](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words), [Part 2](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors), [Part 3](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-3-more-fun-with-word-vectors) + - [What would Shakespeare say (NLP Tutorial)](https://gigadom.wordpress.com/2015/10/02/natural-language-processing-what-would-shakespeare-say/) + - [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) -##Computer Vision + +## Computer Vision - [Awesome computer vision (github)](https://github.com/jbhuang0604/awesome-computer-vision) + - [Awesome deep vision (github)](https://github.com/kjw0612/awesome-deep-vision) + ## Support Vector Machine + - [Highest Voted Questions about SVMs on Cross Validated](http://stats.stackexchange.com/questions/tagged/svm) + - [Help me Understand SVMs!](http://stats.stackexchange.com/questions/3947/help-me-understand-support-vector-machines) + - [SVM in Layman's terms](https://www.quora.com/What-does-support-vector-machine-SVM-mean-in-laymans-terms) + - [How does SVM Work | Comparisons](http://stats.stackexchange.com/questions/23391/how-does-a-support-vector-machine-svm-work) + - [A tutorial on SVMs](http://alex.smola.org/papers/2003/SmoSch03b.pdf) + - [Practical Guide to SVC](http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf), [Slides](http://www.csie.ntu.edu.tw/~cjlin/talks/freiburg.pdf) + - [Introductory Overview of SVMs](http://www.statsoft.com/Textbook/Support-Vector-Machines) + - Comparisons + - [SVMs > ANNs](http://stackoverflow.com/questions/6699222/support-vector-machines-better-than-artificial-neural-networks-in-which-learn?rq=1), [ANNs > SVMs](http://stackoverflow.com/questions/11632516/what-are-advantages-of-artificial-neural-networks-over-support-vector-machines), [Another Comparison](http://www.svms.org/anns.html) + - [Trees > SVMs](http://stats.stackexchange.com/questions/57438/why-is-svm-not-so-good-as-decision-tree-on-the-same-data) + - [Kernel Logistic Regression vs SVM](http://stats.stackexchange.com/questions/43996/kernel-logistic-regression-vs-svm) + - [Logistic Regression vs SVM](http://stats.stackexchange.com/questions/58684/regularized-logistic-regression-and-support-vector-machine), [2](http://stats.stackexchange.com/questions/95340/svm-v-s-logistic-regression), [3](https://www.quora.com/Support-Vector-Machines/What-is-the-difference-between-Linear-SVMs-and-Logistic-Regression) + - [Optimization Algorithms in Support Vector Machines](http://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf) + - [Variable Importance from SVM](http://stats.stackexchange.com/questions/2179/variable-importance-from-svm) + - Software + - [LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) + - [Intro to SVM in R](http://cbio.ensmp.fr/~jvert/svn/tutorials/practical/svmbasic/svmbasic_notes.pdf) + - Kernels - [What are Kernels in ML and SVM?](https://www.quora.com/What-are-Kernels-in-Machine-Learning-and-SVM) + - [Intuition Behind Gaussian Kernel in SVMs?](https://www.quora.com/Support-Vector-Machines/What-is-the-intuition-behind-Gaussian-kernel-in-SVM) + - Probabilities post SVM + - [Platt's Probabilistic Outputs for SVM](http://www.csie.ntu.edu.tw/~htlin/paper/doc/plattprob.pdf) + - [Platt Calibration Wiki](https://en.wikipedia.org/wiki/Platt_scaling) + - [Why use Platts Scaling](http://stats.stackexchange.com/questions/5196/why-use-platts-scaling) + - [Classifier Classification with Platt's Scaling](http://fastml.com/classifier-calibration-with-platts-scaling-and-isotonic-regression/) + ## Reinforcement Learning + - [Awesome Reinforcement Learning (GitHub)](https://github.com/aikorea/awesome-rl) + - [RL Tutorial Part 1](http://outlace.com/Reinforcement-Learning-Part-1/), [Part 2](http://outlace.com/Reinforcement-Learning-Part-2/) + ## Decision Trees + - [Wikipedia Page - Lots of Good Info](https://en.wikipedia.org/wiki/Decision_tree_learning) + - [FAQs about Decision Trees](http://stats.stackexchange.com/questions/tagged/cart) + - [Brief Tour of Trees and Forests](http://statistical-research.com/a-brief-tour-of-the-trees-and-forests/) + - [Tree Based Models in R](http://www.statmethods.net/advstats/cart.html) + - [How Decision Trees work?](http://www.aihorizon.com/essays/generalai/decision_trees.htm) + - [Weak side of Decision Trees](http://stats.stackexchange.com/questions/1292/what-is-the-weak-side-of-decision-trees) + - [Thorough Explanation and different algorithms](http://www.ise.bgu.ac.il/faculty/liorr/hbchap9.pdf) + - [What is entropy and information gain in the context of building decision trees?](http://stackoverflow.com/questions/1859554/what-is-entropy-and-information-gain) + - [Slides Related to Decision Trees](http://www.slideshare.net/pierluca.lanzi/machine-learning-and-data-mining-11-decision-trees) + - [How do decision tree learning algorithms deal with missing values?](http://stats.stackexchange.com/questions/96025/how-do-decision-tree-learning-algorithms-deal-with-missing-values-under-the-hoo) + - [Using Surrogates to Improve Datasets with Missing Values](https://www.salford-systems.com/videos/tutorials/tips-and-tricks/using-surrogates-to-improve-datasets-with-missing-values) + - [Good Article](https://www.mindtools.com/dectree.html) + - [Are decision trees almost always binary trees?](http://stats.stackexchange.com/questions/12187/are-decision-trees-almost-always-binary-trees) + - [Pruning Decision Trees](https://en.wikipedia.org/wiki/Pruning_(decision_trees)), [Grafting of Decision Trees](https://en.wikipedia.org/wiki/Grafting_(decision_trees)) + - [What is Deviance in context of Decision Trees?](http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart) + - Comparison of Different Algorithms + - [CART vs CTREE](http://stats.stackexchange.com/questions/12140/conditional-inference-trees-vs-traditional-decision-trees) + - [Comparison of complexity or performance](https://stackoverflow.com/questions/9979461/different-decision-tree-algorithms-with-comparison-of-complexity-or-performance) + - [CHAID vs CART](http://stats.stackexchange.com/questions/61230/chaid-vs-crt-or-cart) , [CART vs CHAID](http://www.bzst.com/2006/10/classification-trees-cart-vs-chaid.html) + - [Good Article on comparison](http://www.ftpress.com/articles/article.aspx?p=2248639&seqNum=11) + - CART + - [Recursive Partitioning Wikipedia](https://en.wikipedia.org/wiki/Recursive_partitioning) + - [CART Explained](http://documents.software.dell.com/Statistics/Textbook/Classification-and-Regression-Trees) + - [How to measure/rank “variable importance” when using CART?](http://stats.stackexchange.com/questions/6478/how-to-measure-rank-variable-importance-when-using-cart-specifically-using) + - [Pruning a Tree in R](http://stackoverflow.com/questions/15318409/how-to-prune-a-tree-in-r) + - [Does rpart use multivariate splits by default?](http://stats.stackexchange.com/questions/4356/does-rpart-use-multivariate-splits-by-default) + - [FAQs about Recursive Partitioning](http://stats.stackexchange.com/questions/tagged/rpart) + - CTREE + - [party package in R](https://cran.r-project.org/web/packages/party/party.pdf) + - [Show volumne in each node using ctree in R](http://stackoverflow.com/questions/13772715/show-volume-in-each-node-using-ctree-plot-in-r) + - [How to extract tree structure from ctree function?](http://stackoverflow.com/questions/8675664/how-to-extract-tree-structure-from-ctree-function) + - CHAID + - [Wikipedia Artice on CHAID](https://en.wikipedia.org/wiki/CHAID) + - [Basic Introduction to CHAID](https://smartdrill.com/Introduction-to-CHAID.html) + - [Good Tutorial on CHAID](http://www.statsoft.com/Textbook/CHAID-Analysis) + - MARS + - [Wikipedia Article on MARS](https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines) + - Probabilistic Decision Trees + - [Bayesian Learning in Probabilistic Decision Trees](http://www.stats.org.uk/bayesian/Jordan.pdf) + - [Probabilistic Trees Research Paper](http://people.stern.nyu.edu/adamodar/pdfiles/papers/probabilistic.pdf) + ## Random Forest / Bagging + - [Awesome Random Forest (GitHub)**](https://github.com/kjw0612/awesome-random-forest) + - [How to tune RF parameters in practice?](https://www.kaggle.com/forums/f/15/kaggle-forum/t/4092/how-to-tune-rf-parameters-in-practice) + - [Measures of variable importance in random forests](http://stats.stackexchange.com/questions/12605/measures-of-variable-importance-in-random-forests) + - [Compare R-squared from two different Random Forest models](http://stats.stackexchange.com/questions/13869/compare-r-squared-from-two-different-random-forest-models) + - [OOB Estimate Explained | RF vs LDA](https://stat.ethz.ch/education/semesters/ss2012/ams/slides/v10.2.pdf) + - [Evaluating Random Forests for Survival Analysis Using Prediction Error Curve](https://www.jstatsoft.org/index.php/jss/article/view/v050i11) + - [Why doesn't Random Forest handle missing values in predictors?](http://stats.stackexchange.com/questions/98953/why-doesnt-random-forest-handle-missing-values-in-predictors) + - [How to build random forests in R with missing (NA) values?](http://stackoverflow.com/questions/8370455/how-to-build-random-forests-in-r-with-missing-na-values) + - [FAQs about Random Forest](http://stats.stackexchange.com/questions/tagged/random-forest), [More FAQs](http://stackoverflow.com/questions/tagged/random-forest) + - [Obtaining knowledge from a random forest](http://stats.stackexchange.com/questions/21152/obtaining-knowledge-from-a-random-forest) + - [Some Questions for R implementation](http://stackoverflow.com/questions/20537186/getting-predictions-after-rfimpute), [2](http://stats.stackexchange.com/questions/81609/whether-preprocessing-is-needed-before-prediction-using-finalmodel-of-randomfore), [3](http://stackoverflow.com/questions/17059432/random-forest-package-in-r-shows-error-during-prediction-if-there-are-new-fact) + ## Boosting + - [Boosting for Better Predictions](http://www.datasciencecentral.com/profiles/blogs/boosting-algorithms-for-better-predictions) + - [Boosting Wikipedia Page](https://en.wikipedia.org/wiki/Boosting_(machine_learning)) + - [Introduction to Boosted Trees | Tianqi Chen](https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf) + - Gradient Boosting Machine + - [Gradiet Boosting Wiki](https://en.wikipedia.org/wiki/Gradient_boosting) + - [Guidelines for GBM parameters in R](http://stats.stackexchange.com/questions/25748/what-are-some-useful-guidelines-for-gbm-parameters), [Strategy to set parameters](http://stats.stackexchange.com/questions/35984/strategy-to-set-the-gbm-parameters) + - [Meaning of Interaction Depth](http://stats.stackexchange.com/questions/16501/what-does-interaction-depth-mean-in-gbm), [2](http://stats.stackexchange.com/questions/16501/what-does-interaction-depth-mean-in-gbm) + - [Role of n.minobsinnode parameter of GBM in R](http://stats.stackexchange.com/questions/30645/role-of-n-minobsinnode-parameter-of-gbm-in-r) + - [GBM in R](http://www.slideshare.net/mark_landry/gbm-package-in-r) + - [FAQs about GBM](http://stats.stackexchange.com/tags/gbm/hot) + - [GBM vs xgboost](https://www.kaggle.com/c/higgs-boson/forums/t/9497/r-s-gbm-vs-python-s-xgboost) - xgboost + - [xgboost tuning kaggle](https://www.kaggle.com/khozzy/rossmann-store-sales/xgboost-parameter-tuning-template/log) + - [xgboost vs gbm](https://www.kaggle.com/c/otto-group-product-classification-challenge/forums/t/13012/question-to-experienced-kagglers-and-anyone-who-wants-to-take-a-shot/68296#post68296) + - [xgboost survey](https://www.kaggle.com/c/higgs-boson/forums/t/10335/xgboost-post-competition-survey) + - [Practical XGBoost in Python online course (free)](http://education.parrotprediction.teachable.com/courses/practical-xgboost-in-python) + - AdaBoost + - [AdaBoost Wiki](https://en.wikipedia.org/wiki/AdaBoost), [Python Code](https://gist.github.com/tristanwietsma/5486024) + - [AdaBoost Sparse Input Support](http://hamzehal.blogspot.com/2014/06/adaboost-sparse-input-support.html) + - [adaBag R package](https://cran.r-project.org/web/packages/adabag/adabag.pdf) + - [Tutorial](http://math.mit.edu/~rothvoss/18.304.3PM/Presentations/1-Eric-Boosting304FinalRpdf.pdf) + ## Ensembles + - [Wikipedia Article on Ensemble Learning](https://en.wikipedia.org/wiki/Ensemble_learning) + - [Kaggle Ensembling Guide](http://mlwave.com/kaggle-ensembling-guide/) + - [The Power of Simple Ensembles](http://www.overkillanalytics.net/more-is-always-better-the-power-of-simple-ensembles/) + - [Ensemble Learning Intro](http://machine-learning.martinsewell.com/ensembles/) + - [Ensemble Learning Paper](http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/springerEBR09.pdf) + - [Ensembling models with R](http://amunategui.github.io/blending-models/), [Ensembling Regression Models in R](http://stats.stackexchange.com/questions/26790/ensembling-regression-models), [Intro to Ensembles in R](http://www.vikparuchuri.com/blog/intro-to-ensemble-learning-in-r/) + - [Ensembling Models with caret](http://stats.stackexchange.com/questions/27361/stacking-ensembling-models-with-caret) + - [Bagging vs Boosting vs Stacking](http://stats.stackexchange.com/questions/18891/bagging-boosting-and-stacking-in-machine-learning) + - [Good Resources | Kaggle Africa Soil Property Prediction](https://www.kaggle.com/c/afsis-soil-properties/forums/t/10391/best-ensemble-references) + - [Boosting vs Bagging](http://www.chioka.in/which-is-better-boosting-or-bagging/) + - [Resources for learning how to implement ensemble methods](http://stats.stackexchange.com/questions/32703/resources-for-learning-how-to-implement-ensemble-methods) + - [How are classifications merged in an ensemble classifier?](http://stats.stackexchange.com/questions/21502/how-are-classifications-merged-in-an-ensemble-classifier) + ## Stacking Models + - [Stacking, Blending and Stacked Generalization](http://www.chioka.in/stacking-blending-and-stacked-generalization/) + - [Stacked Generalization (Stacking)](http://machine-learning.martinsewell.com/ensembles/stacking/) + - [Stacked Generalization: when does it work?](http://www.ijcai.org/Proceedings/97-2/011.pdf) + - [Stacked Generalization Paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.1533&rep=rep1&type=pdf) + ## Vapnik–Chervonenkis Dimension + - [Wikipedia article on VC Dimension](https://en.wikipedia.org/wiki/VC_dimension) + - [Intuitive Explanantion of VC Dimension](https://www.quora.com/Explain-VC-dimension-and-shattering-in-lucid-Way) + - [Video explaining VC Dimension](https://www.youtube.com/watch?v=puDzy2XmR5c) + - [Introduction to VC Dimension](http://www.svms.org/vc-dimension/) + - [FAQs about VC Dimension](http://stats.stackexchange.com/questions/tagged/vc-dimension) + - [Do ensemble techniques increase VC-dimension?](http://stats.stackexchange.com/questions/78076/do-ensemble-techniques-increase-vc-dimension) + ## Bayesian Machine Learning + - [Bayesian Methods for Hackers (using pyMC)](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) + - [Should all Machine Learning be Bayesian?](http://videolectures.net/bark08_ghahramani_samlbb/) + - [Tutorial on Bayesian Optimisation for Machine Learning](http://www.iro.umontreal.ca/~bengioy/cifar/NCAP2014-summerschool/slides/Ryan_adams_140814_bayesopt_ncap.pdf) + - [Bayesian Reasoning and Deep Learning](http://blog.shakirm.com/2015/10/bayesian-reasoning-and-deep-learning/), [Slides](http://blog.shakirm.com/wp-content/uploads/2015/10/Bayes_Deep.pdf) + - [Bayesian Statistics Made Simple](http://greenteapress.com/wp/think-bayes/) + - [Kalman & Bayesian Filters in Python](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python) + - [Markov Chain Wikipedia Page](https://en.wikipedia.org/wiki/Markov_chain) + ## Semi Supervised Learning + - [Wikipedia article on Semi Supervised Learning](https://en.wikipedia.org/wiki/Semi-supervised_learning) + - [Tutorial on Semi Supervised Learning](http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf) + - [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) + - [Taxonomy](http://is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/taxo_[0].pdf) + - [Video Tutorial Weka](https://www.youtube.com/watch?v=sWxcIjZFGNM) + - [Unsupervised, Supervised and Semi Supervised learning](http://stats.stackexchange.com/questions/517/unsupervised-supervised-and-semi-supervised-learning) -- [Research Papers 1](http://mlg.eng.cam.ac.uk/zoubin/papers/zglactive.pdf), [2](http://mlg.eng.cam.ac.uk/zoubin/papers/zgl.pdf), [3](http://icml.cc/2012/papers/616.pdf) +- [Research Papers 1](http://mlg.eng.cam.ac.uk/zoubin/papers/zglactive.pdf), [2](http://mlg.eng.cam.ac.uk/zoubin/papers/zgl.pdf), [3](http://icml.cc/2012/papers/616.pdf) + ## Optimization + - [Mean Variance Portfolio Optimization with R and Quadratic Programming](http://www.wdiam.com/2012/06/10/mean-variance-portfolio-optimization-with-r-and-quadratic-programming/?utm_content=buffer04c12&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer) -- [Algorithms for Sparse Optimization and Machine -Learning](http://www.ima.umn.edu/2011-2012/W3.26-30.12/activities/Wright-Steve/sjw-ima12) + +- [Algorithms for Sparse Optimization and Machine Learning](http://www.ima.umn.edu/2011-2012/W3.26-30.12/activities/Wright-Steve/sjw-ima12) + - [Optimization Algorithms in Machine Learning](http://pages.cs.wisc.edu/~swright/nips2010/sjw-nips10.pdf), [Video Lecture](http://videolectures.net/nips2010_wright_oaml/) + - [Optimization Algorithms for Data Analysis](http://www.birs.ca/workshops/2011/11w2035/files/Wright.pdf) + - [Video Lectures on Optimization](http://videolectures.net/stephen_j_wright/) + - [Optimization Algorithms in Support Vector Machines](http://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf) + - [The Interplay of Optimization and Machine Learning Research](http://jmlr.org/papers/volume7/MLOPT-intro06a/MLOPT-intro06a.pdf) + ## Other Tutorials + - For a collection of Data Science Tutorials using R, please refer to [this list](https://github.com/ujjwalkarn/DataScienceR). + - For a collection of Data Science Tutorials using Python, please refer to [this list](https://github.com/ujjwalkarn/DataSciencePython). From 5f71c727644c867ffa467b1a993e2fd9f38f982c Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Sat, 1 Apr 2017 15:16:15 +0800 Subject: [PATCH 44/77] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index dcaed24..e6ee977 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ - [Curated list of Python tutorials for Data Science, NLP and Machine Learning](https://github.com/ujjwalkarn/DataSciencePython). -## Table of Contents +## Contents - [Miscellaneous](#general) - [Interview Resources](#interview) - [Artificial Intelligence](#ai) From e29c42023cfa245d3471a704f2504787b333aa47 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Tue, 2 May 2017 11:21:07 +0800 Subject: [PATCH 45/77] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e6ee977..29faa0b 100644 --- a/README.md +++ b/README.md @@ -570,7 +570,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - Recurrent and LSTM Networks - [awesome-rnn: list of resources (GitHub Repo)](https://github.com/kjw0612/awesome-rnn) - - [Recurrent Neural Net Tutorial Part 1](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/), [Part 2] (http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/), [Part 3] (http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/), [Code](https://github.com/dennybritz/rnn-tutorial-rnnlm/) + - [Recurrent Neural Net Tutorial Part 1](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/), [Part 2](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/), [Part 3](http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/), [Code](https://github.com/dennybritz/rnn-tutorial-rnnlm/) - [NLP RNN Representations](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/) From ae1848171046040017c01cbd18d34324b175bb63 Mon Sep 17 00:00:00 2001 From: ethen8181 Date: Fri, 9 Jun 2017 14:32:24 -0500 Subject: [PATCH 46/77] machine learning notebooks --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 29faa0b..2800ac9 100644 --- a/README.md +++ b/README.md @@ -207,12 +207,14 @@ - [AI Junkie](http://www.ai-junkie.com/) - a blog about Artificial Intellingence -- [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/)- Making deep learning accessible +- [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/) - Making deep learning accessible - [J Alammar's Blog](http://jalammar.github.io/)- Blog posts about Machine Learning and Neural Nets - [Adam Geitgey](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.f7vwrtfne) - Easiest Introduction to machine learning +- [Ethen's Notebook Collection](https://github.com/ethen8181/machine-learning) - Continuously updated machine learning documentations (mainly in Python3). Contents include educational implementation of machine learning algorithms from scratch and open-source library usage + ## Resources on Quora From 506e89fc7eab85f0ebe6c384ce61cedf62e5c88e Mon Sep 17 00:00:00 2001 From: Guillaume Chevalier Date: Wed, 5 Jul 2017 02:13:45 -0400 Subject: [PATCH 47/77] Add LSTM for Human Activity Recognition --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 29faa0b..b5df08b 100644 --- a/README.md +++ b/README.md @@ -620,6 +620,8 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Torch code for Visual Question Answering using a CNN+LSTM model](https://github.com/abhshkdz/neural-vqa) + - [LSTM for Human Activity Recognition](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/) + - Gated Recurrent Units (GRU) - [LSTM vs GRU](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/) From ef8647de8854e42d03a66b9921e0ac7cedc7cb9a Mon Sep 17 00:00:00 2001 From: Guillaume Chevalier Date: Wed, 5 Jul 2017 02:20:25 -0400 Subject: [PATCH 48/77] Add Time series forecasting with seq2seq RNN --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 29faa0b..2164dd6 100644 --- a/README.md +++ b/README.md @@ -623,6 +623,8 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - Gated Recurrent Units (GRU) - [LSTM vs GRU](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/) + + - [Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models](https://github.com/guillaume-chevalier/seq2seq-signal-prediction) From 7456cd148d999b871279879c34deae2d170df422 Mon Sep 17 00:00:00 2001 From: Guillaume Chevalier Date: Fri, 21 Jul 2017 16:00:53 -0400 Subject: [PATCH 49/77] Add "Discover structure behind data with decision trees" --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index af7e280..9537edc 100644 --- a/README.md +++ b/README.md @@ -893,6 +893,8 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [What is Deviance in context of Decision Trees?](http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart) +- [Discover structure behind data with decision trees](http://vooban.com/en/tips-articles-geek-stuff/discover-structure-behind-data-with-decision-trees/) - Grow and plot a decision tree to automatically figure out hidden rules in your data + - Comparison of Different Algorithms - [CART vs CTREE](http://stats.stackexchange.com/questions/12140/conditional-inference-trees-vs-traditional-decision-trees) From 93e8c331a5231aa5db5d2c11639c372a0b1ca188 Mon Sep 17 00:00:00 2001 From: EtienneT Date: Wed, 9 Aug 2017 13:57:56 -0400 Subject: [PATCH 50/77] Update README.md This is a great article on how to use cross-validation for model selection (cross-validate your model selection process) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d28dfa3..389633e 100644 --- a/README.md +++ b/README.md @@ -347,7 +347,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Cross Validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) - + - [How to use cross-validation in predictive modeling](http://stuartlacy.co.uk/04022016-crossvalidation) - [Training with Full dataset after CV?](http://stats.stackexchange.com/questions/11602/training-with-the-full-dataset-after-cross-validation) - [Which CV method is best?](http://stats.stackexchange.com/questions/103459/how-do-i-know-which-method-of-cross-validation-is-best) From c75e3fb011a14c210f37999f24f1d47a4cba21cd Mon Sep 17 00:00:00 2001 From: Guillaume Chevalier Date: Fri, 11 Aug 2017 16:07:17 -0400 Subject: [PATCH 51/77] Add Hyperopt tutorial MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters: http://vooban.com/en/tips-articles-geek-stuff/hyperopt-tutorial-for-optimizing-neural-networks-hyperparameters/ --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index d28dfa3..e72d250 100644 --- a/README.md +++ b/README.md @@ -1134,6 +1134,9 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [The Interplay of Optimization and Machine Learning Research](http://jmlr.org/papers/volume7/MLOPT-intro06a/MLOPT-intro06a.pdf) +- [Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters](http://vooban.com/en/tips-articles-geek-stuff/hyperopt-tutorial-for-optimizing-neural-networks-hyperparameters/) + + ## Other Tutorials From c5e58b62f201e7fc8ad428afbb4cac7f29d2ced6 Mon Sep 17 00:00:00 2001 From: Andres Varela Date: Tue, 15 Aug 2017 23:49:48 +0800 Subject: [PATCH 52/77] Add THL to README.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Looking at the millions of #machinelearning tweets already processed, noise is a little over 94%. Looking at the 8,000 daily tweets that are tagged #machineLearning, the site filters and ranks the most popular shared content in realtime. Machine learning’s zeitgeist, you might say. It's been running for over a year, monitoring half a billion tweets a day, and will always be free to use. --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index d28dfa3..df29aa1 100644 --- a/README.md +++ b/README.md @@ -93,6 +93,8 @@ - [Have Fun With Machine Learning](https://github.com/humphd/have-fun-with-machine-learning) +- [Twitter's Most Shared #machineLearning Content From The Past 7 Days](http://theherdlocker.com/tweet/popularity/machinelearning) + ## Interview Resources From 7174251605850efec44d487d1320c2372f4305e4 Mon Sep 17 00:00:00 2001 From: EtienneT Date: Wed, 11 Oct 2017 16:38:20 -0400 Subject: [PATCH 53/77] Updated link with new location --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 389633e..8bc2393 100644 --- a/README.md +++ b/README.md @@ -347,7 +347,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Cross Validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) - - [How to use cross-validation in predictive modeling](http://stuartlacy.co.uk/04022016-crossvalidation) + - [How to use cross-validation in predictive modeling](http://stuartlacy.co.uk/2016/02/04/how-to-correctly-use-cross-validation-in-predictive-modelling/) - [Training with Full dataset after CV?](http://stats.stackexchange.com/questions/11602/training-with-the-full-dataset-after-cross-validation) - [Which CV method is best?](http://stats.stackexchange.com/questions/103459/how-do-i-know-which-method-of-cross-validation-is-best) From e30a396e7d8b2e6b7190486766579e8bec11b2f5 Mon Sep 17 00:00:00 2001 From: Dillon Mulroy Date: Tue, 17 Oct 2017 13:56:15 -0400 Subject: [PATCH 54/77] Fix broken genetic algorithms links. The links for 'Simple Implementation of Genetic Algorithms in Python' were broken. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 82c3a8c..90510ee 100644 --- a/README.md +++ b/README.md @@ -131,7 +131,7 @@ - [Genetic Algorithms Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm) -- [Simple Implementation of Genetic Algorithms in Python (Part 1)](http://outlace.com/Simple-Genetic-Algorithm-in-15-lines-of-Python/), [Part 2](http://outlace.com/Simple-Genetic-Algorithm-Python-Addendum/) +- [Simple Implementation of Genetic Algorithms in Python (Part 1)](http://outlace.com/miniga.html), [Part 2](http://outlace.com/miniga_addendum.html) - [Genetic Algorithms vs Artificial Neural Networks](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks) From 758f728b8da7f8aadf17d195a2a4842067102e0c Mon Sep 17 00:00:00 2001 From: Gaurav Charnalia Date: Tue, 14 Nov 2017 17:16:49 +0530 Subject: [PATCH 55/77] Update README.md Fix Broken Natural Language Processing Link ( tf-idf explained ) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 90510ee..a915a16 100644 --- a/README.md +++ b/README.md @@ -707,7 +707,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) -- [tf-idf explained](http://michaelerasm.us/tf-idf-in-10-minutes/) +- [tf-idf explained](http://michaelerasm.us/post/tf-idf-in-10-minutes/) - [Interesting Deep Learning NLP Projects Stanford](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) From e8149ecf47c03261e7ce72d406a83be47c6c3af9 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Wed, 27 Dec 2017 11:10:08 +0800 Subject: [PATCH 56/77] Update README.md --- README.md | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index a915a16..537a055 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ ## Contents -- [Miscellaneous](#general) +- [Introduction](#general) - [Interview Resources](#interview) - [Artificial Intelligence](#ai) - [Genetic Algorithms](#ga) @@ -51,7 +51,15 @@ -## Miscellaneous +## Introduction + +- [Machine Learning Course by Andrew Ng (Stanford University)](https://www.coursera.org/learn/machine-learning) + +- [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) + +- [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) + +- [List of Machine Learning University Courses](https://github.com/prakhar1989/awesome-courses#machine-learning) - [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) @@ -67,8 +75,6 @@ - [Machine Learning FAQs on Cross Validated](http://stats.stackexchange.com/questions/tagged/machine-learning) -- [List of Machine Learning University Courses](https://github.com/prakhar1989/awesome-courses#machine-learning) - - [Machine Learning algorithms that you should always have a strong understanding of](https://www.quora.com/What-are-some-Machine-Learning-algorithms-that-you-should-always-have-a-strong-understanding-of-and-why) - [Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables](http://terpconnect.umd.edu/~bmomen/BIOM621/LineardepCorrOrthogonal.pdf) @@ -89,8 +95,6 @@ - [TheAnalyticsEdge edX Notes and Codes](https://github.com/pedrosan/TheAnalyticsEdge) -- [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) - - [Have Fun With Machine Learning](https://github.com/humphd/have-fun-with-machine-learning) - [Twitter's Most Shared #machineLearning Content From The Past 7 Days](http://theherdlocker.com/tweet/popularity/machinelearning) @@ -396,6 +400,10 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) ## Deep Learning +- [fast.ai - Practical Deep Learning For Coders](http://course.fast.ai/) + +- [fast.ai - Cutting Edge Deep Learning For Coders](http://course.fast.ai/part2.html) + - [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning) - [Lots of Deep Learning Resources](http://deeplearning4j.org/documentation.html) From 053cea2f6a4acc0b169a803545fb523311f9e9e5 Mon Sep 17 00:00:00 2001 From: rohduggal <35959788+rohduggal@users.noreply.github.com> Date: Fri, 23 Feb 2018 01:27:12 +0530 Subject: [PATCH 57/77] Added curated ML resources https://hackr.io/tutorials/learn-machine-learning-ml --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 537a055..50f2787 100644 --- a/README.md +++ b/README.md @@ -55,6 +55,8 @@ - [Machine Learning Course by Andrew Ng (Stanford University)](https://www.coursera.org/learn/machine-learning) +- [Curated List of Machine Learning Resources](https://hackr.io/tutorials/learn-machine-learning-ml) + - [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) - [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) From 73ccfa03e08a651e5812f2e71709fc728743b5a9 Mon Sep 17 00:00:00 2001 From: qualityjacks Date: Sat, 14 Apr 2018 15:23:02 +0530 Subject: [PATCH 58/77] Added Curated AI Learning Resources https://hackr.io/tutorials/learn-artificial-intelligence-ai --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 537a055..76cfe43 100644 --- a/README.md +++ b/README.md @@ -121,6 +121,8 @@ - [UC Berkeley CS188 Intro to AI](http://ai.berkeley.edu/home.html), [Lecture Videos](http://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM) +- [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai) + - [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/) - [edX course | Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) From 74d78e87badcee6b6ea0c01653616deb83321cab Mon Sep 17 00:00:00 2001 From: Chris Ratcliff Date: Mon, 23 Apr 2018 21:46:54 +0100 Subject: [PATCH 59/77] Add ML Compiled to Cheat Sheets --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 537a055..29647e1 100644 --- a/README.md +++ b/README.md @@ -264,6 +264,8 @@ - [Machine Learning Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet) +- [ML Compiled](https://ml-compiled.readthedocs.io/en/latest/) + ## Classification From 18cd51758b3c334ab473b5f8957147f29a618534 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Wed, 13 Jun 2018 07:59:19 +0800 Subject: [PATCH 60/77] Added interview resources --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 537a055..bdcd9ca 100644 --- a/README.md +++ b/README.md @@ -113,6 +113,8 @@ - [What are the key skills of a data scientist?](https://www.quora.com/What-are-the-key-skills-of-a-data-scientist) +- [The Big List of DS/ML Interview Resources](https://towardsdatascience.com/the-big-list-of-ds-ml-interview-resources-2db4f651bd63) + ## Artificial Intelligence From b8e072034a83c9b1219352d5d858fd10e9c62e3a Mon Sep 17 00:00:00 2001 From: soulbliss Date: Mon, 16 Jul 2018 11:20:41 +0530 Subject: [PATCH 61/77] fixed the broken url to the updated url for "A brief Tour of the Trees and Forest" --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bdcd9ca..2ebb253 100644 --- a/README.md +++ b/README.md @@ -883,7 +883,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [FAQs about Decision Trees](http://stats.stackexchange.com/questions/tagged/cart) -- [Brief Tour of Trees and Forests](http://statistical-research.com/a-brief-tour-of-the-trees-and-forests/) +- [Brief Tour of Trees and Forests](https://statistical-research.com/index.php/2013/04/29/a-brief-tour-of-the-trees-and-forests/) - [Tree Based Models in R](http://www.statmethods.net/advstats/cart.html) From 204ab1ccbd855befefb0e1d733d23816c2a464b7 Mon Sep 17 00:00:00 2001 From: meghpatel Date: Mon, 1 Oct 2018 16:34:18 +0530 Subject: [PATCH 62/77] Added some links in Named Entity Recognition under NLP --- README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/README.md b/README.md index bdcd9ca..e346c1b 100644 --- a/README.md +++ b/README.md @@ -721,6 +721,8 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Interesting Deep Learning NLP Projects Stanford](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) +- [The Stanford NLP Group](https://nlp.stanford.edu/) + - [NLP from Scratch | Google Paper](https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/35671.pdf) - [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) @@ -797,6 +799,12 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Classification Text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) +- Named Entity Recognitation + + - [Stanford Named Entity Recognizer (NER)](https://nlp.stanford.edu/software/CRF-NER.shtml) + + - [Named Entity Recognition: Applications and Use Cases- Towards Data Science](https://towardsdatascience.com/named-entity-recognition-applications-and-use-cases-acdbf57d595e) + - [Language learning with NLP and reinforcement learning](http://blog.dennybritz.com/2015/09/11/reimagining-language-learning-with-nlp-and-reinforcement-learning/) - [Kaggle Tutorial Bag of Words and Word vectors](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words), [Part 2](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors), [Part 3](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-3-more-fun-with-word-vectors) From 247126427742436fac5531f562bf71f8e822f53b Mon Sep 17 00:00:00 2001 From: guillaume-chevalier Date: Sat, 13 Oct 2018 04:38:39 -0400 Subject: [PATCH 63/77] Add 'Multilingual Latent Dirichlet Allocation (LDA)'. --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index bdcd9ca..4097594 100644 --- a/README.md +++ b/README.md @@ -759,6 +759,8 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Topic Modeling of Twitter Followers](http://alexperrier.github.io/jekyll/update/2015/09/04/topic-modeling-of-twitter-followers.html) + - [Multilingual Latent Dirichlet Allocation (LDA)](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA) + - word2vec From 8afcfc46b3753f70c3d9cf94e7b7e908530609cb Mon Sep 17 00:00:00 2001 From: Guillaume Chevalier Date: Sat, 13 Oct 2018 04:42:18 -0400 Subject: [PATCH 64/77] Add "Multilingual Latent Dirichlet Allocation (LDA)" Tutorial. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 4097594..0acd8f0 100644 --- a/README.md +++ b/README.md @@ -759,7 +759,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Topic Modeling of Twitter Followers](http://alexperrier.github.io/jekyll/update/2015/09/04/topic-modeling-of-twitter-followers.html) - - [Multilingual Latent Dirichlet Allocation (LDA)](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA) + - [Multilingual Latent Dirichlet Allocation (LDA)](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA). ([Tutorial here](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA/blob/master/Multilingual-LDA-Pipeline-Tutorial.ipynb)) From 9958dd5f5bd2fea2fb7a396ff94724f6cb32790d Mon Sep 17 00:00:00 2001 From: Benedict Florance Date: Sun, 28 Oct 2018 13:21:30 +0530 Subject: [PATCH 65/77] Add resource for backpropagation --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index bdcd9ca..bedbebc 100644 --- a/README.md +++ b/README.md @@ -450,6 +450,8 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Deep Learning Basics](http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html) +- [Intuition Behind Backpropagation](https://medium.com/spidernitt/breaking-down-neural-networks-an-intuitive-approach-to-backpropagation-3b2ff958794c) + - [Stanford Tutorials](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/) - [Train, Validation & Test in Artificial Neural Networks](http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ) From 3e454b5c48dbe6aa7fee2ff44e80c1bdab80dcb5 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Wed, 2 Jan 2019 06:39:02 +0800 Subject: [PATCH 66/77] Update contributing.md --- contributing.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contributing.md b/contributing.md index 134caf1..8711389 100644 --- a/contributing.md +++ b/contributing.md @@ -5,6 +5,7 @@ If you want to contribute to this list (please do), send me a pull request. Sinc Please ensure your pull request adheres to the following guidelines: - **Please make an individual pull request for each suggestion.** +- Please only submit resources that are completly free to access. - The pull request and commit should have a useful title. - Please search previous suggestions before making a new one, as yours may be a duplicate. - Make sure your link has a useful and relevant title. From 3cebfb52447d317cdbc746bcfe5a257ba37f8d70 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Wed, 2 Jan 2019 06:48:56 +0800 Subject: [PATCH 67/77] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 69b41ff..0e70134 100644 --- a/README.md +++ b/README.md @@ -472,6 +472,8 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - Neural Machine Translation + - **[Machine Translation Reading List](https://github.com/THUNLP-MT/MT-Reading-List#machine-translation-reading-list)** + - [Introduction to Neural Machine Translation with GPUs (part 1)](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) - [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/) From 5d4cf841c70204d9a6507390979f20fd8de03958 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Wed, 2 Jan 2019 07:06:43 +0800 Subject: [PATCH 68/77] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 0e70134..585c31d 100644 --- a/README.md +++ b/README.md @@ -414,6 +414,8 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning) +- **[Deep Learning Papers Reading Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md)** + - [Lots of Deep Learning Resources](http://deeplearning4j.org/documentation.html) - [Interesting Deep Learning and NLP Projects (Stanford)](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) From f0c0f940e9d23f328defa7b50032fd670ed7083a Mon Sep 17 00:00:00 2001 From: Ben Rozemberczki Date: Sat, 26 Jan 2019 20:25:00 +0000 Subject: [PATCH 69/77] Created network representation learning section. I added 5 aggregator/awesome repos of graph based deep learning papers, each has a different flavour. The first two include implementations. The last three reference and aggregate only the papers. Also updated the TOC. --- README.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/README.md b/README.md index 585c31d..8e35456 100644 --- a/README.md +++ b/README.md @@ -32,6 +32,7 @@ - [Restricted Boltzmann Machine, DBNs](#rbm) - [Autoencoders](#auto) - [Convolutional Neural Nets](#cnn) + - [Graph Representation Learning](#nrl) - [Natural Language Processing](#nlp) - [Topic Modeling, LDA](#topic) - [Word2Vec](#word2vec) @@ -720,6 +721,19 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Visualising and Understanding CNNs](https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf) + + +- Network Representation Learning + + - [Awesome Graph Embedding](https://github.com/benedekrozemberczki/awesome-graph-embedding) + + - [Awesome Network Embedding](https://github.com/chihming/awesome-network-embedding) + + - [Network Representation Learning Papers](https://github.com/thunlp) + + - [Knowledge Representation Learning Papers](https://github.com/thunlp/KRLPapers) + + - [Graph Based Deep Learning Literature](https://github.com/naganandy/graph-based-deep-learning-literature) From 70eab97b7b3951febb89267692729a49dfee6a42 Mon Sep 17 00:00:00 2001 From: Espoir Murhabazi Date: Fri, 31 May 2019 10:55:47 +0200 Subject: [PATCH 70/77] Update link for Topic modeling for twitter Update the broken link for twitter topic modeling, the previous link was broken. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8e35456..1b19818 100644 --- a/README.md +++ b/README.md @@ -783,7 +783,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [LDA in Scala](http://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-i-the-theory.html), [Part 2](http://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-ii-the-code.html) - - [Segmentation of Twitter Timelines via Topic Modeling](http://alexperrier.github.io/jekyll/update/2015/09/16/segmentation_twitter_timelines_lda_vs_lsa.html) + - [Segmentation of Twitter Timelines via Topic Modeling](https://alexisperrier.com/nlp/2015/09/16/segmentation_twitter_timelines_lda_vs_lsa.html) - [Topic Modeling of Twitter Followers](http://alexperrier.github.io/jekyll/update/2015/09/04/topic-modeling-of-twitter-followers.html) From 0768abeaa5f5e630c8ea6d40cf96bd921d72917c Mon Sep 17 00:00:00 2001 From: LupusSomniator Date: Thu, 13 Jun 2019 14:25:54 +0700 Subject: [PATCH 71/77] Added CatBoost info in Boosting category --- README.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/README.md b/README.md index 1b19818..d66f6d8 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ + # Machine Learning & Deep Learning Tutorials [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) - This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this [list](https://github.com/sindresorhus/awesome). @@ -1069,6 +1070,18 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Tutorial](http://math.mit.edu/~rothvoss/18.304.3PM/Presentations/1-Eric-Boosting304FinalRpdf.pdf) +- CatBoost + + - [CatBoost Documentation](https://catboost.ai/docs/) + + - [Benchmarks](https://catboost.ai/#benchmark) + + - [Tutorial](https://github.com/catboost/tutorials) + + - [GitHub Project](https://github.com/catboost) + + - [CatBoost vs. Light GBM vs. XGBoost](https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db) + ## Ensembles From 821c422e89689d4f8694686b9853f09d9e9fc0ab Mon Sep 17 00:00:00 2001 From: Lettier Date: Fri, 7 Feb 2020 00:49:14 -0500 Subject: [PATCH 72/77] Adds Your Guide to Latent Dirichlet Allocation --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index d66f6d8..6a76c50 100644 --- a/README.md +++ b/README.md @@ -790,6 +790,8 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Multilingual Latent Dirichlet Allocation (LDA)](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA). ([Tutorial here](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA/blob/master/Multilingual-LDA-Pipeline-Tutorial.ipynb)) + - [Your Guide to Latent Dirichlet Allocation](https://medium.com/@lettier/how-does-lda-work-ill-explain-using-emoji-108abf40fa7d) + - word2vec From e0b457969d2157cc6be61808a380db3b965cad6e Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Sun, 26 Apr 2020 17:18:35 +0800 Subject: [PATCH 73/77] Update README.md --- README.md | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index d66f6d8..587d2f1 100644 --- a/README.md +++ b/README.md @@ -761,14 +761,16 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Topic Modeling](https://en.wikipedia.org/wiki/Topic_model) + - [Topic Modeling Wiki](https://en.wikipedia.org/wiki/Topic_model) + - [**Probabilistic Topic Models Princeton PDF**](http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf) - [LDA](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation), [LSA](https://en.wikipedia.org/wiki/Latent_semantic_analysis), [Probabilistic LSA](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) - - [What is a good explanation of Latent Dirichlet Allocation?](https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation) + - [What is a good explanation of Latent Dirichlet Allocation (LDA)?](https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation) - - [Awesome LDA Explanation!](http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/). [Another good explanation](http://confusedlanguagetech.blogspot.in/2012/07/jordan-boyd-graber-and-philip-resnik.html) + - [**Intrduction to LDA**](http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/), [Another good explanation](http://confusedlanguagetech.blogspot.in/2012/07/jordan-boyd-graber-and-philip-resnik.html) - - [The LDA Buffet- Intuitive Explanation](http://www.matthewjockers.net/2011/09/29/the-lda-buffet-is-now-open-or-latent-dirichlet-allocation-for-english-majors/) + - [The LDA Buffet - Intuitive Explanation](http://www.matthewjockers.net/2011/09/29/the-lda-buffet-is-now-open-or-latent-dirichlet-allocation-for-english-majors/) - [Difference between LSI and LDA](https://www.quora.com/Whats-the-difference-between-Latent-Semantic-Indexing-LSI-and-Latent-Dirichlet-Allocation-LDA) @@ -777,7 +779,8 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [alpha and beta in LDA](http://datascience.stackexchange.com/questions/199/what-does-the-alpha-and-beta-hyperparameters-contribute-to-in-latent-dirichlet-a) - [Intuitive explanation of the Dirichlet distribution](https://www.quora.com/What-is-an-intuitive-explanation-of-the-Dirichlet-distribution) - + - [topicmodels: An R Package for Fitting Topic Models](https://cran.r-project.org/web/packages/topicmodels/vignettes/topicmodels.pdf) + - [Topic modeling made just simple enough](https://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/) - [Online LDA](http://alexminnaar.com/online-latent-dirichlet-allocation-the-best-option-for-topic-modeling-with-large-data-sets.html), [Online LDA with Spark](http://alexminnaar.com/distributed-online-latent-dirichlet-allocation-with-apache-spark.html) @@ -789,7 +792,12 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Topic Modeling of Twitter Followers](http://alexperrier.github.io/jekyll/update/2015/09/04/topic-modeling-of-twitter-followers.html) - [Multilingual Latent Dirichlet Allocation (LDA)](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA). ([Tutorial here](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA/blob/master/Multilingual-LDA-Pipeline-Tutorial.ipynb)) - + - [Deep Belief Nets for Topic Modeling](https://github.com/larsmaaloee/deep-belief-nets-for-topic-modeling) + - [Gaussian LDA for Topic Models with Word Embeddings](http://www.cs.cmu.edu/~rajarshd/papers/acl2015.pdf) + - Python + - [Series of lecture notes for probabilistic topic models written in ipython notebook](https://github.com/arongdari/topic-model-lecture-note) + - [**Implementation of various topic models in Python**](https://github.com/arongdari/python-topic-model) + - word2vec From f124733a1a015b5b80fbbc43d22e228f299c0cc2 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Sun, 26 Apr 2020 17:19:25 +0800 Subject: [PATCH 74/77] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 587d2f1..7e326a8 100644 --- a/README.md +++ b/README.md @@ -768,7 +768,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [What is a good explanation of Latent Dirichlet Allocation (LDA)?](https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation) - - [**Intrduction to LDA**](http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/), [Another good explanation](http://confusedlanguagetech.blogspot.in/2012/07/jordan-boyd-graber-and-philip-resnik.html) + - [**Introduction to LDA**](http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/), [Another good explanation](http://confusedlanguagetech.blogspot.in/2012/07/jordan-boyd-graber-and-philip-resnik.html) - [The LDA Buffet - Intuitive Explanation](http://www.matthewjockers.net/2011/09/29/the-lda-buffet-is-now-open-or-latent-dirichlet-allocation-for-english-majors/) From 9792f01ddaeda6c06c1f2c7dacf55d6ba0ffc1b9 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Sun, 26 Apr 2020 17:27:36 +0800 Subject: [PATCH 75/77] Update README.md --- README.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 5ab5d22..e616ab6 100644 --- a/README.md +++ b/README.md @@ -760,11 +760,11 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) -- [Topic Modeling](https://en.wikipedia.org/wiki/Topic_model) - - [Topic Modeling Wiki](https://en.wikipedia.org/wiki/Topic_model) +- Topic Modeling + - [Topic Modeling Wikipedia](https://en.wikipedia.org/wiki/Topic_model) - [**Probabilistic Topic Models Princeton PDF**](http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf) - - [LDA](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation), [LSA](https://en.wikipedia.org/wiki/Latent_semantic_analysis), [Probabilistic LSA](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) + - [LDA Wikipedia](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation), [LSA Wikipedia](https://en.wikipedia.org/wiki/Latent_semantic_analysis), [Probabilistic LSA Wikipedia](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) - [What is a good explanation of Latent Dirichlet Allocation (LDA)?](https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation) @@ -772,6 +772,8 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [The LDA Buffet - Intuitive Explanation](http://www.matthewjockers.net/2011/09/29/the-lda-buffet-is-now-open-or-latent-dirichlet-allocation-for-english-majors/) + - [Your Guide to Latent Dirichlet Allocation (LDA)](https://medium.com/@lettier/how-does-lda-work-ill-explain-using-emoji-108abf40fa7d) + - [Difference between LSI and LDA](https://www.quora.com/Whats-the-difference-between-Latent-Semantic-Indexing-LSI-and-Latent-Dirichlet-Allocation-LDA) - [Original LDA Paper](https://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf) @@ -793,12 +795,11 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [Multilingual Latent Dirichlet Allocation (LDA)](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA). ([Tutorial here](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA/blob/master/Multilingual-LDA-Pipeline-Tutorial.ipynb)) - - [Your Guide to Latent Dirichlet Allocation](https://medium.com/@lettier/how-does-lda-work-ill-explain-using-emoji-108abf40fa7d) - [Deep Belief Nets for Topic Modeling](https://github.com/larsmaaloee/deep-belief-nets-for-topic-modeling) - [Gaussian LDA for Topic Models with Word Embeddings](http://www.cs.cmu.edu/~rajarshd/papers/acl2015.pdf) - Python - [Series of lecture notes for probabilistic topic models written in ipython notebook](https://github.com/arongdari/topic-model-lecture-note) - - [**Implementation of various topic models in Python**](https://github.com/arongdari/python-topic-model) + - [Implementation of various topic models in Python](https://github.com/arongdari/python-topic-model) From d01d49c489a07f0902e91d32b386c98ccfb6c1cf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stjepan=20Jurekovi=C4=87?= Date: Tue, 7 Sep 2021 15:10:16 +0200 Subject: [PATCH 76/77] Added Grokking Machine Learning Hi, Stjepan from Manning here. I thought this title might be a good match to your list. Thank you for considering it. Best, --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index e616ab6..68026d6 100644 --- a/README.md +++ b/README.md @@ -103,6 +103,8 @@ - [Twitter's Most Shared #machineLearning Content From The Past 7 Days](http://theherdlocker.com/tweet/popularity/machinelearning) +- [Grokking Machine Learning](https://www.manning.com/books/grokking-machine-learning) + ## Interview Resources From 7c5e128e22c6843924aac8f6b0868548bc516690 Mon Sep 17 00:00:00 2001 From: Ujjwal Karn Date: Fri, 1 Oct 2021 10:28:03 -0700 Subject: [PATCH 77/77] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index e616ab6..6605e3d 100644 --- a/README.md +++ b/README.md @@ -57,6 +57,8 @@ - [Machine Learning Course by Andrew Ng (Stanford University)](https://www.coursera.org/learn/machine-learning) +- [AI/ML YouTube Courses](https://github.com/dair-ai/ML-YouTube-Courses) + - [Curated List of Machine Learning Resources](https://hackr.io/tutorials/learn-machine-learning-ml) - [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)