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Mastering PyTorch

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

Last Updated in Jul 2025

Publisher Packt

ISBN-13 9781801074308

Length 554 pages

Edition 2nd Edition

Tools

PyTorch

Concepts

Deep Learning

Author (1):

Ashish Ranjan Jha

View More author details

Table of Contents (22) 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. Other Books You May Enjoy

21. Index

Summary

In this chapter, we have briefly explored how to explain or interpret the decisions made by deep learning models using PyTorch.

In the next chapter of this book, we will learn how to build a recommendation system from scratch, using PyTorch.

The rest of the chapter is locked

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Programming languages

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Authors (1)

Ashish Ranjan Jha

Ashish Ranjan Jha

Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.

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