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Learn the mathematical foundations of backpropagation through detailed hand calculations and step-by-step derivations in this comprehensive 37-minute tutorial. Master the high-level concepts of backpropagation before diving deep into the essential mathematical prerequisites including the multivariate chain rule. Work through a complete forward pass with detailed calculations, then explore the backward pass mechanics where gradients are computed manually for each layer. Discover how to use calculated gradients to update network weights and verify your hand calculations using PyTorch code. Test your understanding with a quiz section and reinforce key concepts through a comprehensive summary, with additional resources including GitHub code for intermediate weight verification and mathematical proofs for the multivariate chain rule.
