https://medium.com/@alejandroescontrela/convolutional-neural-networks-from-the-ground-up-c67bb41454e1

When Yann LeCun published his work on the development of a new kind of neural network architecture [1], the Convolutional Neural Network (CNN), his work went largely unnoticed. It took 14 years and a team of researchers from The University of Toronto to bring CNN’s into the public’s view during the 2012 ImageNet Computer Vision competition. Their entry, which they named AlexNet after chief architect Alex Krizhevsky, achieved an error of only 15.8% when tasked with classifying millions of images from thousands of categories [2]. Fast forward to 2018 and the current state-of-the-art Convolutional Neural Networks achieve accuracies that surpass human-level performance [3].

Motivated by these promising results, I set out to understand how CNN’s function, and how it is that they perform so well. As Richard Feynman pointed out, “What I cannot build, I do not understand”, and so to gain a well-rounded understanding of this advancement in AI, I built a convolutional neural network from scratch in NumPy. After finishing this project I feel that there’s a disconnect between how complex convolutional neural networks appear to be, and how complex they really are. Hopefully, you too will share this feeling after building your own network from scratch.