Machine Learning Math

A collection of 3 posts

Subgradient Descent Explained, Step by Step cover image

Subgradient Descent Explained, Step by Step

Gradient descent is one of the most popular algorithms to train machine learning models. However, many of the popular machine learning models like lasso regression or support vector machines contain loss functions that are not differentiable. Because of this, regular gradient descent can not be used. One of the most commonly utilized techniques to circumvent this issue is to use subgradients instead of regular gradients. And in this article, you will learn how it's done.

  • Boris Giba
Gradient Descent for Linear Regression Explained, Step by Step cover image

Gradient Descent for Linear Regression Explained, Step by Step

Gradient descent is one of the most famous techniques in machine learning and used for training all sorts of neural networks. But gradient descent can not only be used to train neural networks, but many more machine learning models. In particular, gradient descent can be used to train a linear regression model! If you are curious as to how this is possible, or if you want to approach gradient descent with smaller steps and not jump straight to neural networks, this post is for you. You will learn how gradient descent works from an intuitive, visual, and mathematical standpoint and we will apply it to an exemplary dataset in Python.

Vectorization Explained, Step by Step cover image

Vectorization Explained, Step by Step

Vectorization is one of the most useful techniques to make your machine learning code more efficient. In this post, you will learn everything you need to know to start using vectorization efficiently in your machine learning projects.