Artificial neural network fundamentals -- PyTorch fundamentals -- Building deep neural networks with PyTorch -- Introducing convolutional neural networks -- Transfer learning for image classification -- Practical aspects of image classification -- Object detection -- Advanced object detection -- Image segmentation -- Applications of object detection and segmentation -- Autoencoders and image manipulation -- Image generation using GANs -- Advanced GANs for image manipulation -- Training with minimal data points -- Combining computer vision and natural language processing -- Combining computer vision and reinforcement learning -- Moving computer vision models to production -- OpenCV utilities for image analysis.

The book Modern Computer Vision with PyTorch is a practical guide to building deep learning applications for computer vision using the PyTorch framework. Covering more than fifty hands-on projects, it introduces neural networks, convolutional neural networks, transfer learning, object detection, image segmentation, GANs, image captioning, reinforcement learning, and deployment techniques. The book combines theoretical foundations with implementation examples, making it an excellent resource for students, researchers, and AI practitioners developing real-world computer vision systems.