Backpropagation

Backpropagation is an algorithm used to train neural networks by adjusting the weights to minimize the error.

Backpropagation is used in various applications such as image recognition, natural language processing, and game playing. It helps in optimizing the neural network to improve its performance on the given task. The process involves computing the gradient of the loss function with respect to each weight by the chain rule, propagating the error backward through the network. The weights are then updated using gradient descent to minimize the error.

Backpropagation is essential for training deep neural networks as it allows the model to learn from the errors and improve its predictions over time. It is a key component of supervised learning in neural networks.

Understanding and implementing backpropagation is crucial for developing effective neural network models that can make accurate predictions and decisions.