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The Vanishing Gradient Problem Explained: Why ReLU Replaced Sigmoid (Ep:03.02)
Sigmoid’s derivative maxes out at 0.25 and shrinks fast — multiply that through 15 layers and gradients vanish to nearly zero before reaching early layers. Module 3 of From Zero…
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How Multi-Layer Neural Networks Solve XOR (and Why Nonlinearity Is Non-Negotiable) (Ep:03.01)
Stacking two perceptrons solves the problem one perceptron provably can’t. Module 3 of From Zero to Agents hand-builds a working XOR network, proves why linear layers alone can never work…
