This episode explores the implementation of manual backpropagation in neural networks, emphasizing its importance despite the prevalence of automated tools like PyTorch's Autograd. Against the backdrop of readily available Autograd engines, the lecture advocates for understanding backpropagation's internals to debug and optimize neural networks effectively, referring to it as a "leaky abstraction" where ignorance can lead to subtle bugs. The discussion references historical context, noting that manual backpropagation was standard practice a decade ago, contrasting it with today's automated approach. More significantly, the episode details the process of manually calculating gradients for a two-layer multi-layer perceptron, including the derivation of gradients for cross-entropy loss and batch normalization, and the presenter shares a line-by-line walkthrough of the code implementation, offering insights into the mathematical reasoning behind each step. For instance, the presenter explains how Bessel's correction is used for a better variance estimation. The presenter also shares a conceptual understanding of the forces of push and pull in gradients, and how they relate to the probabilities in the forward pass. The exercise ultimately aims to empower listeners with a deeper understanding of neural network training, enabling them to fine-tune models and troubleshoot potential issues more effectively.
