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1. A Hands-On Guide to Deep Learning for Computer Vision with PyTorch
   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.

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2. Great Book ! Must READ
   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.

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3. Amazing Code files
   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.

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4. Detailed explanation of generative AI
   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.

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

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6. Highly practical book for any AI engineer
   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.

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7. Great read – absolutely recommended
   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!!

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8. Must Buy
   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.

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9. The author does an excellent job of explaining complex concepts in a simple and engaging way. The step-by-step approach, combined with practical examples, makes it easy to follow along, even for someone without a deep background in AI or machine learning. The fundamental concepts like image processing, feature detection, and object recognition are broken down into digestible pieces, without assuming much prior knowledge. The book includes plenty of practical exercises, using Python, which helped me apply the theory immediately and the best part is the github link with all ready-to-run codes.

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10. First things first, this is a substantial book, spanning over 700 pages across 18 chapters.The book is promoted as a bridge between academia and practical applications and is aimed towards newbies and intermediate readers – and by and large, it delivers. Starting with an introduction to neural networks and an introduction to PyTorch, these are then combined and the reader is guided through building a neural network using PyTorch.Key computer vision concepts such as CNNs, object detection, and segmentation each get their own chapter. In the middle section, the book moves on to autoencoders, GANs, and reinforcement learning. However, this reader found the chapter on combining CV and NLP techniques particularly fascinating. Chapter 15 discusses vision transformers and their application in OCR – truly intriguing stuff. The book concludes with arguably the most useful chapter on deploying a model to production, covering creating APIs, containerisation, and running containers in the cloud. Highly, highly useful.Each chapter includes Python exercises and test-yourself questions at the end. As usual with Packt books, the book is well-written and thoroughly covers the subject matter in a clear and accessible manner.Highly recommended.

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11. All the required information about computer vision is available in this book. Covers all the way from basics to the most recent advances!

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12. I was looking forward to more interesting use cases for object detection and semantic segmentation.

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13. Learnt a great deal from the details shared about different techniques! The uncovering black-box approach cleared a lot of doubts. Highly recommended

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