Stanford CS229 Machine Learning | Spring 2026 | Lecture 8: Neural Networks 2 (Backprop)
This lecture explores Automatic Differentiation, proving that the gradient computation (backward pass) in neural networks is efficient. By viewing networks as differentiable circuits, backpropagation can be computed in time proportional to the number of parameters.
Read article