PyTorch Deep Learning Hands-On is beginner-friendly but also helps readers to get into the depths of deep learning quickly. In the last couple of years, we have seen deep learning become the new electricity. It has fought its way from academia into industry, helping resolve thousands of enigmas that humans could never have imagined solving without it. The mainstream adoption of deep learning as a go-to implementation was driven mainly by a bunch of frameworks that reliably delivered complex algorithms as efficient built-in methods. This book showcases the benefits of PyTorch for prototyping a deep learning model, for building a deep learning workflow, and for taking a prototyped model to production. Overall, the book concentrates on the practical implementation of PyTorch instead of explaining the math behind it, but it also links you to places that you could fall back to if you lag behind with a few concepts.
PyTorch Deep Learning Hands-On
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PyTorch Deep Learning Hands-On
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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)
Preface
Free Chapter
1. Deep Learning Walkthrough and PyTorch Introduction
2. A Simple Neural Network
3. Deep Learning Workflow
4. Computer Vision
5. Sequential Data Processing
6. Generative Networks
7. Reinforcement Learning
Chapter 8. PyTorch to Production
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Index
Customer Reviews