Convolutional Neural Networks For Beginners | by Mina Ghashami

Fundamentals of convolutional neural networks

I wrote this post as part of my preparation for one of the lectures I taught at Interview Kickstart to prepare professionals to land jobs in top tech companies. If you are in the process of preparing for interviews or just strengthening your foundation, this post might help you too.

In this post, we look into convolutional neural networks and their basics and fundamentals. We will start from what a convolution operation is, and continue with what a convolution layer is and how convolutional networks are built.

Let’s get started.

Convolutional Neural Networks (CNNs) consist of several “convolutional layers”. These layers run the “convolution operation.” Convolution is a fundamental operation in signal and image processing. Let’s first see what this operation is.

What is a convolution operation?

Convolution is the mathematical operation between a kernel (filter) and an input feature map.

The kernel is usually a small matrix e.g. 3×3, or 5×5. The input is always a feature map with height, width, and channels. How convolution operation works is that the kernel slides over the input and computes the dot product between the kernel and local regions of input. This dot product multiplication and summation produces a single value in the output feature map.

As the filter slides over all locations, it generates a 2D activation map called output feature map. For each slide of filter (kernel) over the image or the input feature map, we compute the element-wise dot-product and sum them together. This gives one entry in the output map: