Handwritten notes, guides, and teaching materials on various CS topics
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Comprehensive handwritten notes covering backpropagation, activation functions, and optimization techniques.
Comprehensive guide on implementing light-weight ANN model for MNIST recognition task. You will learn to implement forward, backward, gradient descent all from scratch in JAVA.
Detailed notes on YOLO architecture, implementation details, and comparison with other detectors.
Step-by-step approach to solving DP problems with worked examples and visualization.
Essential linear algebra concepts with applications in machine learning and computer vision.
Complete guide to NumPy, Pandas, and Matplotlib with practical examples and exercises.
Fundamentals of robot kinematics, trajectory planning, and path optimization algorithms.