This blog post is going to break down the meat and potatoes of machine learning algorithms - gradient descent. This builds on top of the ideas presented in my post on linear regression, so I recommend checking out that post first. All that aside, let's get into it. What is gradient descent trying to do? At it's core, the purpose of gradient descent is to figure out some function that would crea...
In this post, I am going to give a textual description for what exactly is linear regression, and how that can be applied to a line. To start, what is linear regression? In short, linear regression is the process of fitting a linear line to a set of data. That is to say if you have a graph with multiple data points plotted, and you want to find what single non-curved line most closely connects ...
In this short post, I want to quickly cover what a neural network is. You will often see neural networks depicted with a series of nodes connected by multiple lines. This image can be helpful to visualize, but apart from the obvious media appeal, doesn't explain what is happening at it's core. Don't get me wrong, I believe the classic diagram is useful for abstracting away the nitty gritty and...