This is the code repository for Deep Learning Projects with PyTorch [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
PyTorch is a Deep Learning framework that is a boon for researchers and data scientists. It supports Graphic Processing Units and is a platform that provides maximum flexibility and speed. With PyTorch, you can dynamically build neural networks and easily perform advanced Artificial Intelligence tasks. The course starts with the fundamentals of PyTorch and how to use basic commands. Next, you’ll learn about Convolutional Neural Networks (CNN) throughan example of image recognition, where you’ll look into images from a machine perspective. The next project shows you how to predict share prices using Recurrent Neural Networks (RNN) and Long Short Term Memory Network (LSTM). Then you’ll learn to work with Self Organizing Maps to detect credit card fraud. After that, it’s time to develop a system using Boltzmann Machines, where you’ll recommend whether to watch a movie or not. We’ll continue withBoltzmann Machines, where you’ll learn to give movie ratings using AutoEncoders. At the end, you’ll get to develop and train a model to recognize a picture or an object from agiven image using Deep Learning, where we’ll not only detect the shape, but also the color of the object. By the end of the course, you’ll be able to start using PyTorch to build Deep Learning models by implementing practical projects in the real world. So, grab this course as it will take you through interesting real-world projects to train your first neural nets.
- Strengthen your foundations by understanding PyTorch and its fundamentals
- Run your first basic commands using PyTorch
- See how to make a Convolutional Neural Network (CNN) for image recognition
- Predict share prices with Recurrent Neural Network and Long Short Term Memory Network (LSTM)
- Detect Credit Card Fraud with self-organizing maps
- Develop a movie recommendation system using Boltzmann Machines
- Use AutoEncoders to develop recommendation systems to rate a movie
- Detect the shape and color of a given picture or an object using PyTorch
To fully benefit from the coverage included in this course, you will need:
This course is for Data Science professionals who would like to practically implement PyTorch and exploit its unique features in their Deep Learning projects. A basic understanding of Deep Learning and Python programming knowledge is assumed.
This course has the following software requirements:
-Familiarity with the Python language
-Basic idea of Numpy and Keras
-Linux or MacOS with Python