You're reading from Modern Computer Vision with PyTorch A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

Product type Paperback

Published in Jun 2024

Publisher Packt

ISBN-13 9781803231334

Length 746 pages

Edition 2nd Edition

Languages

Python

Tools

PyTorch

Concepts

Computer Vision

Authors (2):

V Kishore Ayyadevara

Yeshwanth Reddy

View More author details

Table of Contents (26) Chapters

Preface

1. Section 1: Fundamentals of Deep Learning for Computer Vision

2. Artificial Neural Network Fundamentals FREE CHAPTER

3. PyTorch Fundamentals

4. Building a Deep Neural Network with PyTorch

5. Section 2: Object Classification and Detection

6. Introducing Convolutional Neural Networks

7. Transfer Learning for Image Classification

8. Practical Aspects of Image Classification

9. Basics of Object Detection

10. Advanced Object Detection

11. Image Segmentation

12. Applications of Object Detection and Segmentation

13. Section 3: Image Manipulation

14. Autoencoders and Image Manipulation

15. Image Generation Using GANs

16. Advanced GANs to Manipulate Images

17. Section 4: Combining Computer Vision with Other Techniques

18. Combining Computer Vision and Reinforcement Learning

19. Combining Computer Vision and NLP Techniques

20. Foundation Models in Computer Vision

21. Applications of Stable Diffusion

22. Moving a Model to Production

23. Other Books You May Enjoy

24. Index

Appendix

Learning the basics of reinforcement learning

Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in a given state of an environment, maximizing the notion of cumulative reward.

To understand how RL helps, let’s consider a simple scenario. Imagine that you are playing chess against a computer. Let’s identify the different components involved:

The computer is an agent that has learned/is learning how to play chess.
The setup (rules) of the game constitutes the environment.
As we make a move (take an action), the state of the board (the location of various pieces on the chessboard) changes.
At the end of the game, depending on the result, the agent gets a reward. The objective of the agent is to maximize the reward.

If the machine (agent1) is playing against a human, the number of games that it can play is finite (depending on the number of games the human can play...