The definitive book on computer vision is back and updated with the latest machine learning architecture, including 70+ pages on diffusion models Purchase of the print or Kindle book includes a free eBook in PDF format. 
• Understand the inner workings of neural network architectures and their implementation, including transformers

• Build solutions to real-world computer vision applications using PyTorch

• Get to grips with CLIP and stable diffusion, and test their applications, such as in- and out-painting

The second edition of Modern Computer Vision with PyTorch is fully updated on top of the comprehensive coverage in the first edition to explain and provide practical examples of the latest multimodal models, CLIP and Stable Diffusion. Whether you're 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 shows you how to implement state-of-the-art architectures for real-world examples. You'll discover the best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement multiple use cases of 2D and 3D multi-object detection, segmentation, and human pose detection by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. You'll enter the world of generative AI, with facial generation and manipulation, and discover the impressive capabilities of diffusion models with image creation and in- and out-painting. Finally, you'll move your NN model to production on the AWS Cloud. By the end, you'll be able to leverage modern NN architectures to solve over 30 real-world CV problems confidently.
• Train a NN from scratch with NumPy and PyTorch

• Implement 2D and 3D multi-object detection and segmentation

• Implement few-shot and zero-shot learning for vision tasks

• Combine CV with NLP to perform OCR, image captioning, and object detection

• Combine CV with reinforcement learning to build agents that play pong and self-drive a car

• Manipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGAN

• Learn about and implement diffusion models to harness the power of multimodal generative AI

• Discover the benefits of diffusion models over GANs

This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to master computer vision techniques using deep learning and PyTorch. It's especially useful for those who are just getting started with neural networks, as it will enable you to learn from real-world use cases accompanied by notebooks in GitHub. Basic knowledge of the Python programming language and machine learning is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models from chapter 8 onward.