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 is a field of Artificial Intelligence and Computer Science that focuses on enabling computers to interpret and understand visual information from the world around them. Computer vision and Machine Learning are 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 generative adversarial networks (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 also learn how to leverage the latest computer vision techniques like Yolov8 for object detection, stable diffusion models for image generation, vision transformers for zero-shot object detection, knowledge distillation for compression of neural networks, DINO for self-supervised learning, segment anything models (SAM), NeRF and 3D Gaussian Splatting for 3D scenes synthesis. This book is a valuable resource for professionals, researchers, and students who want to expand their knowledge of advanced computer vision techniques using PyTorch.