Epoch/Batch size/Iteration in machine learning
many of us often get confused with these frequently used terms i.e. epochs, batch size, and Iterations.so let us dive into it and understand them.
Epoch:-
one forward and backward pass of entire training data is called an epoch.
suppose if I set epochs=20 then my model gets trained 20 times
on my entire training data set.
Batch size:-
The number of training examples in one forward and the backward pass is called batch size.
suppose if I have a training data set of 55000 images,
and if I set the batch size to 1000 then at a time 1000 images will get get trained.
totally I get 55000/1000=55 batches approximately.
Iteration:-
Iteration means the number of times the parameters of the algorithm gets updated.
in the above example we got 55 batches for one epoch , we know for every epoch our weights get changed, so we can say we need 55 iterations to complete one epoch.
