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Various ML tasks, currently covering:
- Classification
- Regression
- Clustering (k-means)
- Time-series Analysis (AR Models)
- Dimensionality Reduction (Autoencoding)
- Sequence Models (RNN and LSTMs)
- Image Analysis (CNN for Image Classification)
- How to use canned estimators to train ML models.
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How to implement custom estimators (model_fn & EstimatorSpec).
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A standard metadata-driven approach to build the model feature_column(s) (including numeric features as well as categorical features with 1) vocabulary, 2) hash bucket, and 3) identity.
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Data input pipelines (input_fn) using:
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tf.estimator.inputs.pandas_input_fn,
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tf.train.string_input_producer, and
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tf.data.Dataset APIs to read both .csv and .tfrecords (tf.example) data files
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tf.contrib.timeseries.RandomWindowInputFn and WholeDatasetInputFn for time-series data
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Feature preprocessing and creation as part of reading data (input_fn), for example, sin, sqrt, square, log, boolean comparisons, euclidean distance, etc.
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A standard approach to prepare wide (sparse) and deep (dense) feature_column(s) for Wide and Deep DNN Liner Combined Models
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The use of normalizer_fn in numeric_column() to scale the numeric features using pre-computed statistics (for Min-Max or Standard scaling)
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The use of weight_column in the canned estimators
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Implicit Feature Engineering as part of defining feature_colum(s), including:
- crossing,
- clipping,
- embedding,
- indicators (categorical features), and
- bucketization
- How to use the tf.contrib.learn.experiment APIs to train, evaluate, and export models
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Howe to use the tf.estimator.train_and_evaluate function (along with trainSpec & evalSpec) train, evaluate, and export models
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How to serve exported model (export_savedmodel) using csv and json inputs
- Early-stopping implementation
- DynamicRnnEstimator and the use of variable-length sequences
- Collaborative Filtering for Recommendation Models
- Text Analysis (Text Classification, Topic Models, Word/Doc embedding, etc.)
- tf.Transform to preprocessing and feature engineering
- keras examples
