Backpropagation in CNN (Convolutional Neural Networks) is an algorithm used to train the network by adjusting the weights and biases based on the error calculated during the forward pass. It involves two main steps: forward pass and backward pass.

During the forward pass, input data is passed through the network, and predictions are made. The error between the predicted output and the actual target is then calculated.

In the backward pass, the error is propagated back through the layers of the network, starting from the last layer. The gradients of the weights and biases are computed using the chain rule and the error from the previous layer. These gradients indicate the direction and magnitude of the weight adjustments needed to minimize the error.

The computed gradients are then used to update the weights and biases in each layer, typically using an optimization algorithm such as stochastic gradient descent (SGD). This process is repeated iteratively for a number of epochs until the network learns to make accurate predictions.

Backpropagation allows the network to learn from its mistakes and adjust its parameters to improve its performance. It is a key component in training CNNs and plays a crucial role in optimizing the network's ability to recognize and classify visual patterns.