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Neural networks learn through a process called backpropagation:

1. Forward Pass: Input data flows through layers of neurons, each applying weights and activation functions.
2. Loss Calculation: The output is compared to the expected result, calculating an error (loss).
3. Backward Pass: The error is propagated backward, adjusting weights to minimize future errors.
4. Iteration: This process repeats thousands of times with different data samples.

def train(model, data, epochs):
    for epoch in range(epochs):
        for batch in data:
            predictions = model.forward(batch)
            loss = compute_loss(predictions, targets)
            gradients = model.backward(loss)
            model.update_weights(gradients)