How Does Backpropagation Work?
Backpropagation is a fundamental algorithm used for training artificial neural networks. It efficiently computes gradients of the loss function with respect to each weight by applying the chain rule of calculus. The process can be divided into two main phases: forward propagation and backward propagation.
1. Forward Propagation
During forward propagation, input data is fed into the network layer by layer. Each neuron applies an activation function to the weighted sum of its inputs, generating an output. This process continues until the output layer is reached, producing predictions.
2. Loss Calculation
Once the network has made predictions, the loss function is used to evaluate the error by comparing predicted outputs against the actual labels. Common loss functions include Mean Squared Error for regression and Cross-Entropy Loss for classification tasks.
3. Backward Propagation
In this phase, backpropagation begins by calculating the gradient of the loss with respect to the output activations. This gradient is then propagated backward through the network, updating the weights using the gradients computed at each layer. The weights are adjusted using an optimization algorithm, such as Stochastic Gradient Descent (SGD), to minimize the loss.
By iteratively applying forward and backward propagation, the neural network gradually improves its accuracy on the training data. Through numerous iterations, backpropagation helps the model learn intricate patterns by minimizing the loss function, enabling effective generalization to unseen data.