Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI 2nd ed. Edition

Modern Computer Vision with PyTorch” by V Kishore Ayyadevara and Yeshwanth Reddy is a comprehensive guide for those looking to master deep learning techniques for computer vision. The book provides a well-structured introduction to PyTorch, covering fundamental concepts like convolutional neural networks (CNNs), object detection, segmentation, and generative models. It balances theoretical explanations with hands-on coding examples, making it accessible for both beginners and experienced practitioners. The real-world applications and case studies enhance its practicality. If you’re interested in leveraging PyTorch for cutting-edge vision tasks, this book is an excellent resource for building, training, and deploying deep learning models efficiently.

In today's fast-paced tech landscape, understanding the 'why' behind your actions is crucial, and this book excels in teaching that. It not only explains what needs to be done in various scenarios but also explains why these steps are necessary.The book further supports learning with hands-on code examples and thorough explanations of each code block, bridging the gap between theory and practical application seamlessly.

I have gone through almost all the code files shared in the book. The code snippets corresponding to each use-case is extremely well-organized. It’s a delight to have working, well-structured code files and understand the reason why they are structured the way they are from the book. I was able to get inspiration from the provided code files and modify them for my use case quickly. Big thanks to the authors.Strongly recommend this book to anyone who appreciates learning through practical examples.

I really enjoyed how this book makes the connection between NLP and computer vision easy to understand. The book provides detailed explanation of how transformers & diffusion models work with multiple examples. It also covers a deep under-the-hood detail of how different blocks of these models work.The explanations made it easy for me to connect multiple dots and gain a strong intuition of Generative AI. The additional topics on traditional computer vision tasks make the book highly resourceful.

Vertical lines in the text/images over the whole book. Bad printing.

This book covers a host of use-cases with modern techniques - Detectron2, GANs, Deep Fakes, self-driving car, Atari games, Multi-modal AI, Diffusion models, Model deployment, vector stores.Highly worth the price to have everything that a modern AI engineer/ data scientist needs. If you are already a data scientist who is looking to catch up with the latest trends or someone who wants to get into this field, do not miss this book.

I bought the first edition of this book which already covers a majority of computer vision. The revised edition takes the breadth to the next level by including quite a few techniques in the Generative AI world.I’ve been recommending the first edition to many and now will be recommending this!!

A valuable reference book for an AI engineer.Anyone who wants to do practise AI concepts , this book has the best guided exercises .Also the exercises are very close to industry problems.