Conv2d: Finally Understand What Happens in the Forward Pass

Deep Learning's libraries and platforms such as Tensorflow, Keras, Pytorch, Caffe or Theano help us in our daily lives so that every day new applications make us think "Wow!". We all have our favorite framework, but what they all have in common is that they make things easy for us with functions that are easy to use that can be configured as needed. But we still need to understand what the arguments available are to take advantage of all the power these frameworks give us. In this post, I will try to list all these arguments. This post is for you if you want to see their impact on the computation time, the number of trainable parameters and the size of the convolved output channels.