You're reading from Mastering PyTorch Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

Product type Paperback

Published in May 2024

Publisher Packt

ISBN-13 9781801074308

Length 558 pages

Edition 2nd Edition

Tools

PyTorch

Concepts

Deep Learning

Author (1):

Ashish Ranjan Jha

View More author details

Table of Contents (21) Chapters

Preface

1. Overview of Deep Learning Using PyTorch

2. Deep CNN Architectures FREE CHAPTER

3. Combining CNNs and LSTMs

4. Deep Recurrent Model Architectures

5. Advanced Hybrid Models

6. Graph Neural Networks

7. Music and Text Generation with PyTorch

8. Neural Style Transfer

9. Deep Convolutional GANs

10. Image Generation Using Diffusion

11. Deep Reinforcement Learning

12. Model Training Optimizations

13. Operationalizing PyTorch Models into Production

14. PyTorch on Mobile Devices

15. Rapid Prototyping with PyTorch

16. PyTorch and AutoML

17. PyTorch and Explainable AI

18. Recommendation Systems with PyTorch

19. PyTorch and Hugging Face

20. Index

To get the most out of this book

To fully benefit from this book, it is necessary that you meet the following prerequisites and recommendations:

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, a working experience 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 13 as well as to facilitate distributed training in Chapter 12 over several virtual machines.

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/arj7192/MasteringPyTorchV2. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/gbp/9781801074308.

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: “Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system.”

A block of code is set as follows:

def forward(self, source):
    source = self.enc(source) * torch.sqrt(self.num_inputs)
    source = self.position_enc(source)
    op = self.enc_transformer(source, self.mask_source)
    op = self.dec(op)
    return op

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

def forward(self, source):
    source = self.enc(source) * torch.sqrt(self.num_inputs)
    source = self.position_enc(source)
    op = self.enc_transformer(source, self.mask_source)
    op = self.dec(op)
    return op

Any command-line input or output is written as follows:

loss improvement on epoch: 1
[001/200] train: 1.1996 - val: 1.0651
loss improvement on epoch: 2
[002/200] train: 1.0806 - val: 1.0494
...

Bold: Indicates a new term, an important word, or words that you see on the screen. For instance, words in menus or dialog boxes appear in the text like this. For example: “Select System info from the Administration panel.”

Warnings or important notes appear like this.

Tips and tricks appear like this.