Dive into a comprehensive 2.5-hour video tutorial on neural networks and backpropagation, focusing on building micrograd from scratch. Learn step-by-step explanations of key concepts, starting with basic derivatives and progressing to implementing a full neural network library. Follow along as the instructor demonstrates manual backpropagation, builds core components, and compares implementations with PyTorch. Gain practical experience by creating datasets, writing loss functions, and performing gradient descent optimization. Conclude with a summary of modern neural network concepts and a walkthrough of the complete micrograd codebase on GitHub. Suitable for those with basic Python knowledge and a general understanding of calculus.