Kurt Willis

The focus of this article will be on the math behind simple neural networks and implementing the code in python from scratch. Also, the differences between binary and multiclass models will be highlighted.

In machine learning, the backpropagation algorithm requires us to calculate the gradient of a loss or cost functions $c:\R^{m\x n}\to \R$. In doing so, the chain rule is applied and derivatives of intermediate matrix functions $f:\R^{m\x n}\to \R^{m\x n}$ have to be evaluated. This article aims to showcase a practical approach in evaluating such expressions and to further an understanding of the derivative and its use in backpropagation.

K-nearest neighbors is a simple form of machine-learning. It is an algorithm designed to extrapolate and generalize from a given dataset. This post will cover implementing the algorithm in python with the final goal of applying it to the cifar-10 dataset.