Convolutional Neural Networks from the ground up – Towards Data Science

CNN's are best known for their ability to recognize patterns present in images, and so the task chosen for the network described in this post was that of image classification. One of the most common benchmarks for gauging how well a computer vision algorithm performs is to train it on the MNIST handwritten digit database: a collection of 70,000 handwritten digits and their corresponding labels. The goal is to train a CNN to be as accurate as possible when labeling handwritten digits (ranging from 0–9). After about five hours of training and two loops over the training set, the network presented here was able to achieve an accuracy of 98% on the test data, meaning it could correctly guess almost every handwritten digit shown to it. Let's go over the individual components that form the network and how they link together to form predictions from the input data.