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Modern Computer Vision with PyTorch
Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI , Second Edition

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Profile Icon V Kishore Ayyadevara Profile Icon Yeshwanth Reddy
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Can$61.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (19 Ratings)
eBook Jun 2024 746 pages 2nd Edition
eBook
Can$61.99
Paperback
Can$77.99
Subscription
Free Trial
Arrow left icon
Profile Icon V Kishore Ayyadevara Profile Icon Yeshwanth Reddy
Arrow right icon
Can$61.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (19 Ratings)
eBook Jun 2024 746 pages 2nd Edition
eBook
Can$61.99
Paperback
Can$77.99
Subscription
Free Trial
eBook
Can$61.99
Paperback
Can$77.99
Subscription
Free Trial

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Key benefits

  • Understand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion models
  • Build solutions for real-world computer vision problems using PyTorch
  • All the code files are available on GitHub and can be run on Google Colab

Description

Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks. The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion. You’ll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You’ll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you’ll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you’ll learn best practices for deploying a model to production. By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.

Who is this book for?

This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to learn computer vision techniques using deep learning and PyTorch. It's useful for those just getting started with neural networks, as it will enable readers to learn from real-world use cases accompanied by notebooks on GitHub. Basic knowledge of the Python programming language and ML is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models in the latter part of the book.

What you will learn

  • Get to grips with various transformer-based architectures for computer vision, CLIP, Segment-Anything, and Stable Diffusion, and test their applications, such as in-painting and pose transfer
  • Combine CV with NLP to perform OCR, key-value extraction from document images, visual question-answering, and generative AI tasks
  • Implement multi-object detection and segmentation
  • Leverage foundation models to perform object detection and segmentation without any training data points
  • Learn best practices for moving a model to production

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 10, 2024
Length: 746 pages
Edition : 2nd
Language : English
ISBN-13 : 9781803240930
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Product Details

Publication date : Jun 10, 2024
Length: 746 pages
Edition : 2nd
Language : English
ISBN-13 : 9781803240930
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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(19 Ratings)
5 star 73.7%
4 star 5.3%
3 star 5.3%
2 star 15.8%
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Khawaja Muddassar Oct 01, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
An insightful and comprehensive guide that not only demystifies modern computer vision techniques but also includes practical code examples for real-world implementation. A must-read for both beginners and seasoned professionals looking to stay ahead in the field!
Feefo Verified review Feefo
Aditya Jun 20, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
dr t Jun 22, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Kiran A. Jul 06, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Umang Shah Jul 07, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
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