Ml-Basics

This is the fourth post in this series on the basics of Machine Learning. These posts are intended to serve as companion pieces to this zine on binary classification. In the last one, we learnt how adding hidden layers to a Multilayer Perceptron helps it learn increasingly complex decision boundaries. The MLPs used till now made use of the sigmoid function as the activation function. In this post, we’ll move our focus to a much simpler kind of activation function: The Rectifier. ...

This is the third post in this series on the basics of Machine Learning. In the last one, we learnt how a Multilayer Perceptron can be trained to non-linearly segment a dataset. We also saw how a simple artificial neuron forms the building block of a Multilayer Perceptron — or a neural network in general — which can learn much more complicated decision boundaries. Let’s move on to datasets that are harder to segment. One way to improve the learning capability of a MLP is to add more neurons in the form of hidden layers. In this post we’ll explore MLPs with 2 hidden layers. ...

This is the second post in this series on the basics of Machine Learning. The last post detailed the functioning of an artificial neuron, and how it can be trained to linearly segment a dataset. However, most real world datasets are not linearly separable, which begs the question: What is the point of learning about a neuron? Well, by the end of this post, we’ll see that a bunch of neurons, when stacked together, can learn to create powerful non-linear solution spaces. Let’s see how that works. ...

This is the first post in this series on the basics of Machine Learning. My aim here is to create a comprehensive catalogue of ML concepts so that I can quickly refer to them in the future, as well as be of help to anybody in a position similar to mine. This post complements the first segment in the zine: Linear Binary Classification. The idea is to have the content here supplement that in the zine. ...