Neural Networks Demystified: Part 4
summary
Derives exact gradients DJ/DW1 and DJ/DW2 for a 2-layer network, showing DJ/DW matrices match W1 and W2 shapes and the output is a 3x1 backprop error delta3. Implements the math in Python as cost_function_prime using NumPy elementwise multiply and dot, derives sigmoid_prime, uses A2^T · delta3 and X^T multiplies to sum per-example gradients, runs batch gradient descent in a nine-dimensional weight space and prepares numerical gradient checking.
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