Scratch

a large language model is a decoder-only transformer trained on one absurdly simple objective — predict the next token — scaled until the emergent behaviour stops looking simple. 𐃏 this page is the map from that objective to a deployed assistant: the loss, the scaling laws that size the model, the pretrain-align pipeline, and the inference tricks. the hands-on build is nanogpt.

a convolutional neural network is a feedforward network with its linear layers put on a diet: instead of every unit seeing every input, each unit sees a small local window, and every window is processed by the same small set of weights. 𐃏 this page builds the operation from its definition, gets the geometry formulas straight, walks the canonical architectures, and ends with a convolution written in loops and checked against scipy (Goodfellow, Ian, 2016).