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C O M P U T E R V I S I O N : F O U N D AT I O N S A N D A P P L I C AT I O N S

R A N J AY K R I S H N A

A foundational study of computer vision and image processing, covering how computers extract meaningful information from digital images and understand visual content. The material introduces core concepts such as pixels, color, linear algebra for vision, filtering, convolution, edge detection, feature detection, image segmentation, image recognition, object detection, tracking, and image manipulation. It emphasizes implementing computer-vision algorithms through practical programming projects and explores applications such as robotics, autonomous vehicles, medical imaging, image search, and augmented reality.

Computer Vision Deep Learning

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Neural Networks

StanFord University

A comprehensive introduction to neural networks and their role in machine learning and artificial intelligence. The material explains fundamental concepts such as artificial neurons, network architectures, weights, activation functions, learning algorithms, and backpropagation. It also introduces how neural networks can be trained to solve classification and prediction problems, providing a foundation for understanding modern deep learning techniques.

Deep Learning Neural Networks

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Deep Learning with PyTorch

Eli Stevens ,Luca Antiga ,Thomas Viehmann

A practical guide to building and training deep learning models using PyTorch. The book introduces PyTorch tensors, neural networks, data loading, model training, loss functions, monitoring, and visualization through hands-on projects. It focuses on applying deep learning to real-world problems, including image classification and medical imaging, while teaching techniques such as data augmentation, model improvement, and fine-tuning.

Deep Learning Python Pytorch

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Artificial Intelligence A Modern Approach Third Edition

Stuart Russell and Peter Norvig

Artificial Intelligence: A Modern Approach, Third Edition by Stuart Russell and Peter Norvig is a comprehensive textbook covering the theory and practical foundations of Artificial Intelligence. It explores intelligent agents, problem-solving and search, logical reasoning, knowledge representation, probabilistic reasoning, machine learning, reinforcement learning, natural language processing, computer vision, robotics, and AI planning. The book presents AI through an agent-based approach, combining theoretical concepts with algorithms, examples, and exercises.

AI ML(Machine Learning) Deep Learning Robotics

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