I am reading the book Neural Networks and Deep Learning by Micheal Nielsen. In the second chapter of his book, he describes the following algorithm for updating weights and biases for a neural network:
In the 2nd step, the algorithm computes the error for a sample and backpropogates it through the layers. This happens for all of the training samples (please correct me if I am wrong because this is what I assume is going on). Then in the 3rd step, the weights and biases are updated. My question is, why does the algorithm wait until the third step to update the weights and biases. That is, why can't it do it after each training sample instead of after a whole set of training samples?
Thank you so much for your help and I appreciate it.