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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Section 4: PyTorch in Production Systems

In this section, we will explore how to use PyTorch as a powerful tool for running light to extremely heavy deep learning applications. We'll then explore the journey of building a model and putting it into a live production system while grasping some optimization tricks along the way. We'll also dig deeper into the topics of neural architecture search, as well as explainability in AI. The final chapter of this book covers the various additional tools that can be used to accelerate model prototyping and productionizing in PyTorch.

Upon completing this section, you will have an understanding of using PyTorch in production systems at industrial scale. You will be further equipped with the latest PyTorch tools such as Captum and fast.ai and skills such as distributed training and AutoML that are crucial for building advanced deep learning systems.

This section comprises the following chapters: