Lecture 11 expands a logistic unit into a multilayer network: linear layers produce z, activations produce a, and multiple neurons jointly learn a feature transform trained through a final loss.
Lecture 12 treats a network as a computation graph: the forward pass stores intermediates, the backward pass propagates upstream gradients, and local linear, activation, and softmax rules compute every parameter gradient efficiently.
Week 6 uses public neural-network slides for weighted sums, nonlinearities, loss, and backpropagation, then a public LLM/Transformer deck labeled 2025 for decoder-only architecture without treating it as the 2026 live transcript.
Lecture 3 decomposes neural-network training into computation graphs, local derivatives, and the chain rule: the forward pass computes a result; backprop accumulates gradients from the output so every parameter knows how to move.
HW5 avoids automatic differentiation so learners must track forward shapes, caches, and backward gradients themselves.
HW7 builds on HW5 backpropagation to address deep-model architecture and training failures, emphasizing diagnosis over merely adding layers.