Creating Convolutional Neural Networks from Scratch:

Photo by Zoltan Tasi on Unsplash

In recent years, Convolutional Neural Networks have been the first choice for Image processing in Machine Learning projects. Let’s look at how Convolutional Neural Networks work and try to create one for ourselves.

What are Convolutional Neural Networks?

Convolutional Neural Networks are an optimized type of neurons, that can extract features from a n-dimensional array.

Credit to:https://icecreamlabs.com/2018/08/19/3x3-convolution-filters%E2%80%8A-%E2%80%8Aa-popular-choice/

Here is how a Convolutional Neural Network functions:

Step 1: A filter is generated.

Step 2: The filter is applied on every part of the image, even in overlapped regions. (Look at animation above for visual aid).

Step 3: Multiply the filter weights by the region covered by the filter.

Step 4: Sum all the values together.

With these four steps, a full forward pass of a Convolutional layer is complete.

Going Through The Code:

Step 1| Dependencies:

import numpy as np
from matplotlib import pyplot as plt