Backpropagation calculus | Chapter 4, Deep learning

This podcast segment provides a formal, calculus-based explanation of the backpropagation algorithm, building on a previous intuitive walkthrough. The speaker focuses on how machine learning practitioners apply the chain rule in neural networks, starting with a simple network of single neurons per layer to illustrate the calculation of the cost function's sensitivity to weights and biases. The explanation then extends to networks with multiple neurons per layer, detailing how the chain rule is adapted to account for multiple paths of influence on the cost function. The segment emphasizes that understanding these chain rule expressions is crucial for determining the gradient components used in minimizing network cost.