Backpropagation is a fundamental algorithm used to train artificial neural networks by adjusting their weights to minimize the difference between the predicted and actual outputs. This process involves propagating the error backward through the network, allowing it to learn from its mistakes and improve performance over time.
How Backpropagation Works:
- Forward Pass: Input data is passed through the network, layer by layer, to produce an output.
- Error Calculation: The difference between the network’s output and the actual target is computed using a loss function.
- Backward Pass: The error is propagated back through the network, calculating the gradient of the loss function with respect to each weight by applying the chain rule of calculus.
- Weight Update: The weights are adjusted in the opposite direction of the gradient to reduce the error, typically using optimization algorithms like gradient descent.