In this article we will cover Generalised Additive Models (GAMs). We'll cover the base case by modelling a response variable with a univariate smooth function. We'll then build on this by incorporating multiple exogenous variables to create additive models. After then, the GAM can be covered.

In this article, we will cover what containerisation means and we look at Docker, a containerisation platform. Furthermore, we will cover the key commands and concepts needed to create your own containers in docker. What is Docker? Docker is an open-source software platform that assists the deployment of applications. It

In this article we will cover the ARMA and ARIMA based statistical models, cover the maths behind these models, and look at the statistical test required prior and the post the model fitting. I'd recommend reading the previous article here before reading the remainder of this article. In

Today's article will look at one of the more difficult areas in probability called martingale theory. We will cover the basic theory and look at 2 examples which use martingale theory (the drunk man and the ABRACADABRA problem). The Theory Before jumping into the problem, we have to

What are APIs Before we just into the class of Rest APIs it is best to understand what is an API. An API is simply an acronym for application programming interface, a software intermediary that allows two applications to talk to each other. They are usually explained in terms of

This article will cover what is a time series, what does it mean for a time series to be stationary, and how could an autoregressive process or moving average process be used to model the time series. Before starting this article, I would like to mention the Cambridge notes from

Graphs have been used across many fields due to their ability to represent relationships between entities with applications including social networks, search engines, and protein-protein interaction networks. However, one growing limitation of these graphs are the amount of computational resources they require with some large-scale graphs having millions of nodes

Over the past 15 years we have seen a surge of use of Graph Neural Networks (GNNs) being used to model social networks, recommendations systems, transportation networks, and many more systems. With the ever growing use of GNNs, this has naturally led to the questions about the use of GNNs

Introduction This article will delve into diffusion models, a group of latent variable (see definitions) generative models with applications in image generation, audio synthesis, and denoising. More specifically, this article will mostly focus on the derivations and the ideas behind diffusion models, with a heavy enthuses on the ideas introduced

Summary This article explores dynamic programming (DP), a technique used to tackle complex problems in computer science. We will specifically apply DP to two problems involving palindrome partitioning. Requirements A basic understanding of dynamic programming and some experience with DP problems are recommended before reading this article. Definitions • Palindrome: A

One of the most crucial hyperparameters in any machine learning (ML) model is the learning rate. A small learning rate often results in longer training times and can lead to overfitting. Conversely, a large learning rate may accelerate initial training but risks hindering the model’s convergence to the global

Introduction This article will cover the key features of Kullback-Leibler Divergence (KL divergence), a formula invented in 1951 by the mathematicians Soloman Kullback and Richard Leibler. This formula is used in the background of many of the modern day machine learning models focused around probabilistic modelling. These including Variational Autoencoders

Today we'll be looking at a classic problem in probability: "The Ant Problem". The Question: An ant leaves its anthill in order to forage for food. It moves with the speed of 10cm per second, but it doesn't know where to go, therefore every

Generative Adversarial Networks (GANs) were first introduced in 2014 by Ian Goodfellow in his paper “Generative Adversarial Nets.” This paper presented the GAN framework, which consists of two neural networks called the generator and the discriminator. The generator takes random noise as input and outputs a generated image. The discriminator

Machine Learning (ML) has numerous applications in medicine, including disease diagnosis, drug development, predictive healthcare, and more. One key application of ML in medicine is biomedical image processing. These types of models takes an image as an input and then assigns a class label to each pixel in a process

Summary Graphs are incredibly useful for modelling a range of relationships and interactions. Using nodes to represent entities and edges to represent connections between these entities, they have become a very useful representation tool. Nowadays they are used to model social networks, protein-protein interactions, recommendations systems, knowledge graphs, supply chains,

For the past 30 years, tree-based algorithms such as Adaboost and Random Forests have been the go-to methods for solving tabular data problems. While neural networks (NNs) have been used in this context, they have historically struggled to match the performance of tree-based methods. Despite recent advancements in NN capabilities