Stanford's CS229 Machine Learning course, taught by the teaching team including Chris Ré and Tengyu Ma, continues with this lecture on neural network architecture. The session covers how individual neurons combine into layers, how layers stack into deep networks, and the design choices that determine a network's capacity and behavior, building on the course's earlier treatment of supervised learning and optimization. Delivered as a standard graduate lecture with a chalkboard and slide-driven format typical of CS229, it sets up the mathematical groundwork for later lectures on training these architectures with backpropagation. At roughly eighty minutes, it runs at full lecture length and assumes familiarity with the linear algebra and calculus foundations covered earlier in the course.