Backpropagation — How Neural Networks Learn — Free AI & ML Tutorial
Learn Backpropagation — How Neural Networks Learn in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Backpropagation — How Neural Networks Learn in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syl…
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Backpropagation — How Neural Networks Learn in AI & ML
Backpropagation (backprop) is the algorithm that trains neural networks. It efficiently computes how much each weight contributed to the prediction error, then adjusts all weights simultaneously to reduce that error.
Forward pass: Input flows through the network layer by layer, producing a prediction. The loss (error) is computed by comparing prediction to actual label.
Backward pass: Using the chain rule of calculus, gradients of the loss are computed with respect to every weight in the network — starting from the output and working backward. This gives us the direction to adjust each weight.
Weight update: Each weight is adjusted by: w = w - learning_rate × gradient. This is gradient descent. Repeat thousands of times over the training data (each pass = one epoch) until the loss converges.
Backpropagation — How Neural Networks Learn — Syntax
# Backprop chain rule (2-layer network): # Forward: z1 = W1·x + b1 → a1 = relu(z1) → z2 = W2·a1 + b2 → output # Loss: L = (output - target)² # Backward: # dL/dW2 = dL/doutput × doutput/dz2 × dz2/dW2 # dL/dW1 = dL/doutput × ... × dz1/dW1 (chain rule) # Update: W -= lr × gradient
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