In this article I consider the task of clustering a collection of vectors into groups or clusters of vectors that are close to each other, as measured by the distance between pairs of them. In particular I focus on the famous clustering method, called the k-means algorithm, and give some typical applications.
Automatic text summarization (ATS) techniques offer powerful solutions for generating accurate and informative summaries from textual data content. In our digital age, where an estimated 403 million terabytes of data are generated daily, it is vital that we are able to distil large amounts of textual content into focused summaries, containing just the salient details. In this article I provide an updated survey of state-of-the-art ATS methods, with a particular focus on how large language models models address the complexities and nuances of automated text summarization.
To understand even the simplest machine learning algorithm requires a solid grasp of the basics of linear algebra. We can think of linear algebra as a foundational tool that provides the mathematical framework necessary for when we later build and interpret sophisticated machine learning models. In this article, I'll guide you through the core concepts of linear algebra, starting from simple scalar arithmetic and progressing to the complexities of matrix multiplication.
Topic modelling is a form of text analysis that uses unsupervised machine learning to identify patterns, themes, clusters, and groups across a collection of documents. In this article I discuss using the powerful BERTopic library alongside quantized large language models to identify themes and topics from a collection of research papers at the intersection of artificial intelligence and ophthalmology. Then, we'll use the DataMapPlot library to produce a publication ready visualization of the thematic structure contained within the abstracts of those research papers.
Google Colab (or Colaboratory) is a complete, modern, cloud-based runtime environment. It gives individuals and teams the ability to work together on coding, data science and machine learning problems with shared access to data, state of the art GPU's and TPU's, and industry standard Python libraries. You are provided with an executable document, i.e. a notebook much like a Jupyter Notebook, that allows both code and markdown to be written and executed, and your results visualized. In this article I'll cover one of the most fundamental requirements all teams will face - how to get data into your Colab notebook in the first place!
Streamlit is an open-source Python app framework that allows you to rapidly turn your data into an interactive data visualization - all in pure Python code. The apps you create are custom, fast, beautiful and very easy to share. What's more is that Streamlit seamlessly integrates with existing popular Python data visualization tools such as Matplotlib and Plotly Express. In this article we'll take some Twitter data and explore it in the context of building a Streamlit interactive data visualization.