Neural Networks & Deep Learning explicateurs.
Laisse tomber les docs de 40 pages. Chaque explicateur transforme une idée complexe d'IA, de Claude Code, de MCP ou de cloud en un diagramme animé que tu peux faire glisser, scruber et casser — pour que le concept clique en minutes, pas en heures.
Tous les explicateurs Neural Networks & Deep Learning
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.
Arrête de lire à propos. Commence à scruber.
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