#neural-network

2 articles

01 ·Deep Learning Basics·FREE·7 min read Neural Networks from Scratch — From One Neuron to Many Layers What a single neuron actually computes, and why stacking layers is pointless without an activation function — shown with a one-line proof that composing linear maps just gives you another linear map. Metaphor, math, an interactive figure, then fifteen lines of numpy. 02 ·Deep Learning Basics·FREE·9 min read Backpropagation from Scratch — It Is All Just the Chain Rule Why you can get gradients for ten million parameters for roughly the cost of one forward pass. The chain rule, computational graphs, a two-layer network worked by hand with real numbers, and where vanishing gradients come from — every symbol explained as it appears.