In this post, we will attempt to conceptualize Deep Neural Networks (DNN) and apply one to a common problem. We’ll train a version of a DNN called a Multilayer Perceptron (or vanilla network) to classify images from the MNIST database. The MNIST database contains 70,000 handwritten digits from 0-9 and is one of the most famous datasets in machine learning. If all this sounds confusing so far, don’t worry we’ll start at the beginning.
If you want to follow along with the code, the notebook can be found here.