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Multi-Layer Perceptron (MLP) — How Neural Networks Build Features | datarekha
Stack neurons into input, hidden, and output layers, fully connected by weights. See the magic one neuron lacked — early layers detect edges, later layers combine them into shapes and objects — and why activation functions between layers are what stop the network collapsing into a single line. Meet the universal approximation theorem: this ...
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