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How do you backpropagate with a Convolutional Neural Network?
How does the chain rule apply to Convolutional Neural Networks?
In Convolutional Neural Networks, each layer contains filters that learn to extract features from the input data. During backpropagation, the gradients of the loss function are computed with respect to the output of the layer. These gradients are then propagated backwards through the layer, and the gradients of the loss function with respect to the input of the layer are computed using the chain rule. These gradients are then used to update the weights in the layer during training.