Convolutional layer hacking with Python and Numpy

If you are starting to work with convolutional layers in deep learning you may be confused at times with the mix of parameters, computations and channels involved. From stripe to padding, input and output channels, kernels and learnable parameters, there is a lot going on. In this article, we are going to go deep till the very bottom of what goes on within these conv layers. First, we initialize the libraries we will need. This will allow us to code both with pytorch and with the numpy library so that we can compare their results later.