Modern Computer Vision with PyTorch E-Book

V Kishore Ayyadevara

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 836

Veröffentlichungsjahr: 2024

Beschreibung

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.

Leseprobe

Modern Computer Vision with PyTorch

Second Edition

A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

V Kishore Ayyadevara

Yeshwanth Reddy

Modern Computer Vision with PyTorch

Second Edition

Copyright © 2024 Packt Publishing

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First published: November 2020

Second edition: June 2024

Production reference: 2311225

Published by Packt Publishing Ltd.

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11 St Paul’s Square

Birmingham

B3 1RB, UK.

ISBN 978-1-80323-133-4

www.packt.com

Contributors

About the authors

Kishore Ayyadevara is an entrepreneur and a hands-on leader working at the intersection of technology, data, and AI to identify and solve business problems. With over a decade of experience in leadership roles, Kishore has established and grown successful applied data science teams at American Express and Amazon, as well as a top health insurance company. In his current role, he is building a start-up focused on making AI more accessible to healthcare organizations. Outside of work, Kishore has shared his knowledge through his five books on ML/AI, is an inventor with 12 patents, and has been a speaker at multiple AI conferences.

I would like to dedicate this book to my dear parents, Hema and Subrahmanyeswara Rao, my lovely wife, Sindhura, my dearest daughter, Hemanvi, and my beloved son, Tejas. This would not have been possible without their patience, support, and encouragement.

Special thanks to the reviewers for their helpful feedback. This book would not have been in this shape without the great support and feedback I received from Raghav Bali, Prassanna Venkatesh, Sreevaatsav Bavana, and the Packt team - Shruti, Parvathy, Aneri, and Pranit.

Yeshwanth Reddy is a senior data scientist with industry experience spanning over 8 years, specializing in healthcare, education, and document extraction domains. He has given several talks and has mentored thousands of students on a broad spectrum of topics, ranging from statistics to deep learning. His innovative solutions include the development of products and libraries for document extraction and the creation of synthetic data to enhance real-world datasets. Yeshwanth has also contributed to multiple open-source libraries and holds various patents.

I would like to thank my dear parents, Lalitha and Ravi, my beloved wife, Madhuri, and my brother, Sumanth. Your unwavering support and encouragement have been the driving force behind this book. I extend my heartfelt gratitude to the reviewers for their invaluable feedback throughout the authorship journey.

About the reviewers

Raghav Bali is a Staff Data Scientist at Delivery Hero, one the world’s leading food delivery service based out of Berlin, Germany. Raghav has published multiple peer-reviewed papers, has authored more than 7 books, and is a co-inventor of 10+ patents in the areas of ML, deep learning, healthcare, and natural language processing. He has 12+ years of experience working across organizations such as Intel, American Express, Infosys, UnitedHealth Group, and DeliveryHero, developing enterprise-level solutions for real world use-cases.

I would like to take this opportunity to thank the whole team at Packt for their support in making the review process as smooth as possible. I would also like to thank my family for all the support and countless coffee cups. Finally, I would like to wish Kishore and Yeshwanth all the very best for this amazingly enhanced second edition of their already successful book

Sheallika Singh is a deep learning expert and advisor to multiple ML startups. Currently, she is a Staff Machine Learning Engineer, responsible for developing personalization models used by billions of users worldwide. Sheallika has also played a pivotal role in advancing self-driving car technology. She has published research in and serves as a program committee member for top-tier ML conferences. Before entering the industry, Sheallika conducted research on font-free character recognition. She holds a Master’s degree in Data Science from Columbia University and a Bachelor of Science degree in Mathematics and Scientific Computing, with a minor in Industrial Management, from the Indian Institute of Technology Kanpur.

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Section 1

Fundamentals of Deep Learning for Computer Vision

In this section, we will learn what the basic building blocks of a neural network are, and what the role of each block is, in order to successfully train a neural network. In this part, we will first briefly look at the theory of neural networks, before moving on to building and training neural networks with the PyTorch library.

This section comprises the following chapters:

Chapter 1, Artificial Neural Network FundamentalsChapter 2, PyTorch FundamentalsChapter 3, Building a Deep Neural Network with PyTorch