Mastering Computer Vision with PyTorch and Machine Learning

This book, together with the accompanying Python codes, provides a thorough and extensive guide for mastering advanced computer vision techniques for image processing by using the open-source machine learning framework PyTorch. Known for its user-friendly interface and Python programming style, PyTorch is accessible and one of the most popular tools among researchers and practitioners in the field of artificial intelligence. Computer vision and machine learning have become closely related fields. Machine learning is used in computer vision to enable computers to automatically find patterns and relationships in large datasets of images and videos. With a focus on practical applications, this book covers essential concepts such as Kullback Leibler Divergence, maximum likelihood, convolutional neural networks (CNN), generative adversarial networks (GAN), Wasserstein GANs (WGAN), WGAN with gradient penalty (WGAN-GP), Information maximizing generative adversarial networks (infoGAN), variational autoencoders (VAE), and their applications for image classification/ image generation. Readers will learn how to leverage the latest computer vision techniques such as Yolov8 for object detection, Stable Diffusion Models for image generation, Vision Transformers for zero-shot object detection, Knowledge Distillation for compression of neural networks, and Self-distillation with no labels for image segmentation. This book is a valuable resource for professionals, researchers, and students who want to expand their knowledge of advanced computer vision techniques using PyTorch. With clear explanations, practical examples, and real-world use cases, readers will learn how to apply computer vision techniques to image analysis tasks and develop skills necessary to build and train their own models for advanced image analysis. Whether you are a beginner or an experienced data scientist, this book will provide you with the knowledge and tools you need to succeed.