How Does Backpropagation Work?
Backpropagation is a supervised learning algorithm used to train artificial neural networks. It works by minimizing the error between the predicted output and the actual output through a process of gradient descent. The steps involved are as follows:
- Forward Pass: The input data is passed through the network, layer by layer, until the output is generated. This process involves applying weights and activation functions to each neuron's input.
- Calculate Loss: Once the output is obtained, the loss (or error) is calculated using a loss function. This function quantifies how far off the predictions are from the actual labels.
- Backward Pass: The loss is then propagated back through the network. This involves computing the gradient of the loss with respect to each weight using the chain rule of calculus. The aim is to determine how much each weight contributed to the error.
- Update Weights: After computing the gradients, the weights are updated using an optimization algorithm, such as Stochastic Gradient Descent (SGD). Weights are adjusted in the opposite direction of the gradient to minimize the loss.
This process is repeated for multiple epochs, continually refining the weights to improve the model's accuracy. Backpropagation is essential for training deep learning models and enables them to learn complex patterns in data.