Neural Networks & Deep Learning explainers.
Skip the 40-page docs. Every explainer turns a tricky AI, Claude Code, MCP, or cloud idea into a live, animated diagram you can drag, scrub, and break — so the concept finally clicks in minutes, not hours.
Every Neural Networks & Deep Learning explainer
The Transformer Architecture, Block by Block
Every modern LLM is a stack of identical Transformer blocks. Walk through one block, then see why stacking 32, 64, 96 of them changes everything.
Attention: How Models Decide What Matters
Attention is a soft lookup — every token asks every other token "are you relevant?" and weights the answer. See it move with sliders.
Backpropagation: How a Network Actually Learns
Backprop is just credit assignment — blame each parameter for the error, in proportion. Tune learning rate and batch size to see training stabilise or diverge.
Neurons, Layers, and Why Depth Matters
A neuron is a weighted sum followed by a kink. Stack a million in layers and you get a function that approximates almost anything.
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