You're reading from Python Deep Learning Understand how deep neural networks work and apply them to real-world tasks

Product type Paperback

Published in Nov 2023

Publisher Packt

ISBN-13 9781837638505

Length 362 pages

Edition 3rd Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Author (1):

Ivan Vasilev

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Table of Contents (17) Chapters

Preface

1. Part 1:Introduction to Neural Networks

2. Chapter 1: Machine Learning – an Introduction FREE CHAPTER

3. Chapter 2: Neural Networks

4. Chapter 3: Deep Learning Fundamentals

5. Part 2: Deep Neural Networks for Computer Vision

6. Chapter 4: Computer Vision with Convolutional Networks

7. Chapter 5: Advanced Computer Vision Applications

8. Part 3: Natural Language Processing and Transformers

9. Chapter 6: Natural Language Processing and Recurrent Neural Networks

10. Chapter 7: The Attention Mechanism and Transformers

11. Chapter 8: Exploring Large Language Models in Depth

12. Chapter 9: Advanced Applications of Large Language Models

13. Part 4: Developing and Deploying Deep Neural Networks

14. Chapter 10: Machine Learning Operations (MLOps)

15. Index

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16. Other Books You May Enjoy

To get the most out of this book

Many code examples in the book require the presence of a GPU. Don’t worry if you don’t have one. To avoid any hardware limitations, all code examples are available as Jupyter notebooks, executed on Google Colab. So, even if your hardware is not sufficient to run the examples, you can still run them under Colab.

Software/hardware covered in the book	Operating system requirements
PyTorch 2.0.1	Windows, macOS, or Linux
TensorFlow 2.13	Windows (legacy support), macOS, or Linux
Hugging Face Transformers 4.33	Windows, macOS, or Linux

Some code examples in the book may use additional packages not listed in the table. You can see the full list (with versions) in the requirements.txt file in the book’s GitHub repo.

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

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Table of Contents (17) Chapters

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Table of Contents (17) Chapters

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