Experiments – Pedro Leitão

Deblurring, a Classic Machine Learning Problem

Machine Learning

train a variational autoencoder to deblur images

Mondrianiser: Image Inpainting with VAEs

Machine Learning

an example of generative models using a variational autoencoder

Adam’s Apple

the Adam optimisation algorithm

Machine Learning

A key component of training deep learning models is the choice of optimisation algorithm. There are several approaches, ranging from :link simple…

Maps and More Maps

sometimes a map and a few lines of code are all you need

Software Engineering

Recently I was trying to figure out sun exposure for a specific location, except I was a couple of thousand kilometers away, and needed to get a…

How GPU’s work, an explainer using the Mandelbrot set

mandelbrot sets and parallelism

Every day pretty much all of us either uses or hears about the mythical GPU, the Graphics Processing Unit. It’s the thing that makes your games…

Reinforcement Learning - a Primer using Connect Four

the basics of reinforcement learning using connect four as an example, with the gymnasium and baselines libraries

Machine Learning

Reinforcement Learning

One of the mainstay algorithms in machine learning is reinforcement learning (or RL for short). RL is an approach to machine learning that is used…

Regularisation in Machine Learning

a practical guide in overfitting prevention

Machine Learning

Regularisation is a technique designed to prevent models from overfitting. In other words, it helps your model generalise better to unseen data by…

A Classical Machine Learning Problem: Predicting Customer Churn

using machine learning to solve a common business problem

Machine Learning

Customer churn, where customers stop using a company’s services, is a major concern for businesses as it directly impacts revenue. Traditionally…

Text Tasks without Neural Networks

random forests and embeddings for sentiment analysis

Machine Learning

Natural language processing (NLP) is often associated with deep learning and neural networks. However, there are efficient methods for text…

The Basics of Unsupervised Learning: Segmenting an Image

a quick primer on what unsupervised learning means and how it can be used for image segmentation

Machine Learning

Unsupervised Learning

Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a dataset without pre-existing labels and with…

Machine Learning and Predictive Maintenance

using machine learning for a common industrial and engineering application

Machine Learning

Predictive maintenance leverages machine learning to analyze operational data, anticipate potential failures, and schedule timely maintenance. This…

Time Series Forecasting with Prophet

predicting the future with machine learning

Time Series Analysis

Machine Learning

Forecasting future trends is a common application in time series analysis. In this experiment, we will use Meta’s Prophet library to predict trends…

Understanding Random Forest Classification and Its Effectiveness

why random forests and ensemble methods are the underrated heroes of machine learning

Machine Learning

A Random Forest is a versatile and robust machine learning algorithm used for both classification and regression tasks. It builds upon the concept…

Instance vs Model Learning

a comparison of two machine learning approaches

Machine Learning

Instance-based machine learning and model-based machine learning are two broad categories of machine learning algorithms that differ in their…

Which Car is Best ? Analysing and Predicting MOT Test Results

dive deeper into data analysis using a real-world dataset

Machine Learning

In this experiment, we will be analysing the MOT test results of cars in the UK. The MOT test is an annual test of vehicle safety, roadworthiness…

A Wine Quality Prediction Experiment with SKLearn Pipelines

pipelining in machine learning, for fun and profit

Machine Learning

In this experiment, let us use the Wine Quality Dataset from Kaggle to predict the quality of wine based on its features. We will investigate the…

Evaluating Dimensionality Reduction - PCA vs t-SNE

reducing dimensions for better insights

Machine Learning

Evaluating the effectiveness of dimensionality reduction techniques, such as Principal Component Analysis (PCA) and t-Distributed Stochastic…

Basics of Word Vectors

understanding what word vectors are, and what they mean in modern natural language processing

Machine Learning

Word vectors are a mainstay of NLP, and are used in a variety of tasks, from sentiment analysis to machine translation. In this experiment, we will…