Understanding Backpropagation: Calculating Gradients for Hidden Layer Weights and Biases

Short summary

This tutorial derives gradient formulas for hidden layer weights and biases using backpropagation and the chain rule applied to a two-neuron network with softplus activation. It shows how error flows backward through the network by tracing partial derivatives at each layer. Combined with output layer calculations from prior articles, this completes the full backpropagation algorithm ready for code implementation.

• •Step-by-step mathematical derivation of dSSR/dw1, dSSR/db1, dSSR/dw2, dSSR/db2 using chain rule
• •Demonstrates how each hidden layer parameter affects final prediction through intermediate activations
• •Part of a series; ready for practical implementation after derivations

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