Book Image

PyTorch Deep Learning Hands-On

By : Sherin Thomas, Sudhanshu Passi
Book Image

PyTorch Deep Learning Hands-On

By: Sherin Thomas, Sudhanshu Passi

Overview of this book

PyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with PyTorch. It is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly. PyTorch Deep Learning Hands-On shows how to implement the major deep learning architectures in PyTorch. It covers neural networks, computer vision, CNNs, natural language processing (RNN), GANs, and reinforcement learning. You will also build deep learning workflows with the PyTorch framework, migrate models built in Python to highly efficient TorchScript, and deploy to production using the most sophisticated available tools. Each chapter focuses on a different area of deep learning. Chapters start with a refresher on how the model works, before sharing the code you need to implement it in PyTorch. This book is ideal if you want to rapidly add PyTorch to your deep learning toolset.
Table of Contents (11 chapters)
10
Index

Summary

In this chapter, we learned about a whole new field of unsupervised learning: reinforcement learning. It is a whole different field and we have just touched on this topic in this chapter. We learned how to phrase a problem for reinforcement learning, and then we trained a model that sees a few measurements provided by the environment and can learn how to balance a cartpole. You can apply the same knowledge to teach robots to walk, to drive cars, and also to play games. This is one of the more physical applications of deep learning.

In the next and closing chapter, we'll be looking at productionizing our PyTorch models so that you can run them on any framework or language, and scale your deep learning applications.