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

The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion models
 
Key Features
- 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
 
Book 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 this book is for
This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to …