You're reading from Mastering PyTorch Build powerful neural network architectures using advanced PyTorch 1.x features

Product type Paperback

Published in Feb 2021

Publisher Packt

ISBN-13 9781789614381

Length 450 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Deep Learning

Author (1):

Ashish Ranjan Jha

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Table of Contents (20) Chapters

Preface

1. Section 1: PyTorch Overview

2. Chapter 1: Overview of Deep Learning using PyTorch FREE CHAPTER

3. Chapter 2: Combining CNNs and LSTMs

4. Section 2: Working with Advanced Neural Network Architectures

5. Chapter 3: Deep CNN Architectures

6. Chapter 4: Deep Recurrent Model Architectures

7. Chapter 5: Hybrid Advanced Models

8. Section 3: Generative Models and Deep Reinforcement Learning

9. Chapter 6: Music and Text Generation with PyTorch

10. Chapter 7: Neural Style Transfer

11. Chapter 8: Deep Convolutional GANs

12. Chapter 9: Deep Reinforcement Learning

13. Section 4: PyTorch in Production Systems

14. Chapter 10: Operationalizing PyTorch Models into Production

15. Chapter 11: Distributed Training

16. Chapter 12: PyTorch and AutoML

17. Chapter 13: PyTorch and Explainable AI

18. Chapter 14: Rapid Prototyping with PyTorch

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To get the most out of this book

Hands-on Python experience as well as basic knowledge of PyTorch is expected. Because most exercises in this book are in the form of notebooks, experience of working with Jupyter notebooks is expected. Some of the exercises in some of the chapters might require a GPU for faster model training, and therefore having an NVIDIA GPU is a plus. Finally, having registered accounts with cloud computing platforms such as AWS, Google Cloud, and Microsoft Azure will be helpful to navigate parts of Chapter 10, Operationalizing PyTorch Models into Production, and Chapter 11, Distributed Training, where you will distribute training over several virtual machines.

If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.