Hands-On Deep Learning Algorithms with Python

Backward propagation

Once we have predicted the output, we compute the loss, . We use the mean squared error as the loss function, that is, the mean of the squared difference between the actual output, , and the predicted output, , which is given as follows:

Now, we will see how can we use backpropagation to minimize the loss . In order to minimize ...