i hope you are doing well , i want to ask a question regarding loss function in a neural network
i know that the loss function is calculated for each data point in the training set , and then the backpropagation is done depending on if we are using batch gradient descent (backpropagation is done after all the data points are passed) , mini-batch gradient descent(backpropagation is done after batch) or stochastic gradient descent(backpropagation is done after each data point).
how can n be the number of data points ?, because if we calculate the loss after each data point then n would be only 1 everytime.
also i saw a video in where they put n as the number of nodes in the output layer. link to video( you can find what i'm talking about in 5:45) : https://www.youtube.com/watch?v=Zr5viAZGndE&t=5s
therefore iam pretty confused on how we calculate the loss function ? and what does n represent? also when we have multiple inputs, will we only be concerned with the output that the weight we are trying to update influence ? thanks in advance