Neural Networks and Backpropagation: Part Two

This article continues from Neural Networks and Backpropagation: Part One and finishes working through the example of creating a neural network to compute an OR gate.  If you have yet to read through the article and the corresponding Jupyter notebook you should take the time now as it is important for understanding the rest of the working. […]

Neural Networks and Backpropagation: Part One

Lately I’ve been implementing my own neural network code base.  I’ve had a play around with some of the common libraries such as Lasagne and TensorFlow and while these libraries are great, it is important to understand the content itself.  Firstly is the thorough understanding of what exactly the network is doing; which in my opinion is certainly worth […]

Linear Regression


Other than being useful in its own right, understanding and being able to implement linear regression is an important first stepping stone to understanding and executing some more complicated algorithms such as perceptrons and neural networks.  So let’s take that first step to bigger things! Don’t forget to grab the jupyter notebook for this blog post […]