Modern Computer Vision with PyTorch: A Practical Roadmap From Deep Learning Fundamentals to Advanced Applications and Generative AI, 2nd Edition

Description

The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion modelsKey FeaturesUnderstand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion modelsBuild solutions for real-world computer vision problems using PyTorchAll the code files are available on GitHub and can be run on Google ColabBook DescriptionWhether 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.What you will learnGet 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 transferCombine CV with NLP to perform OCR, key-value extraction from document images, visual question-answering, and generative AI tasksImplement multi-object detection and segmentationLeverage foundation models to perform object detection and segmentation without any training data pointsLearn best practices for moving a model to productionWho this book is forThis 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.Table of ContentsArtificial Neural Network FundamentalsPyTorch FundamentalsBuilding a Deep Neural Network with PyTorchIntroducing Convolutional Neural NetworksTransfer Learning for Image ClassificationPractical Aspects of Image ClassificationBasics of Object DetectionAdvanced Object DetectionImage SegmentationApplications of Object Detection and SegmentationAutoencoders and Image ManipulationImage Generation Using GANsAbout the AuthorKishore 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.Yeshwanth Reddy is a highly accomplished data scientist manager with 9+ years of experience in deep learning and document analysis. He has made significant contributions to the field, including building software for end-to-end document digitization, resulting in substantial cost savings. Yeshwanth's expertise extends to developing modules in OCR, word detection, and synthetic document generation. His groundbreaking work has been recognized through multiple patents. He has also created a few Python libraries. With a passion for disrupting unsupervised and self-supervised learning, Yeshwanth is dedicated to reducing reliance on manual annotation and driving innovative solutions in the field of data science.