This episode explores the inner workings of neural network training, focusing on the creation of micrograd, a scalar-valued autograd engine designed for pedagogical purposes. Andrej details how micrograd facilitates the understanding of backpropagation, the core algorithm for tuning neural network weights, by building mathematical expressions and expression graphs. Against the backdrop of scalar operations, Andrej elucidates the concept of derivatives and the chain rule, demonstrating how micrograd calculates gradients through forward and backward passes. More significantly, the lecture transitions to neural networks, illustrating how they are essentially mathematical expressions where backpropagation adjusts weights to minimize loss functions, improving network accuracy. Andrej also addresses the efficiency trade-offs of scalar operations versus tensor operations in production-level libraries like PyTorch, further explaining how micrograd simplifies the understanding of neural network training by abstracting away the complexities of tensors. The discussion culminates in a step-by-step implementation of micrograd, including the value object, forward and backward passes, and the training of a two-layer multi-layer perceptron, reflecting emerging industry patterns of simplifying complex algorithms for educational purposes.
