Honestly Uncertain | Towards Data Science
Ethical issues aside, should you be honest when asked how certain you are about some belief? Of course, it depends. In this blog post, you’ll learn on what. A probabilistic quiz game David Spiegelhalter’s new (as of 2025) fantastic book, “The Art of Uncertainty” – a must-read for everyone who deals with probabilities and their communication […]
Learning How to Play Atari Games Through Deep Neural Networks
In July 1959, Arthur Samuel developed one of the first agents to play the game of checkers. What constitutes an agent that plays checkers can be best described in Samuel’s own words, “…a computer [that] can be programmed so that it will learn to play a better game of checkers than can be played by […]
Tutorial: Semantic Clustering of User Messages with LLM Prompts
As a Developer Advocate, it’s challenging to keep up with user forum messages and understand the big picture of what users are saying. There’s plenty of valuable content — but how can you quickly spot the key conversations? In this tutorial, I’ll show you an AI hack to perform semantic clustering simply by prompting LLMs! […]
On-Device Machine Learning in Spatial Computing
The landscape of computing is undergoing a profound transformation with the emergence of spatial computing platforms(VR and AR). As we step into this new era, the intersection of virtual reality, Augmented Reality, and on-device machine learning presents unprecedented opportunities for developers to create experiences that seamlessly blend digital content with the physical world. The introduction […]
Learnings from a Machine Learning Engineer — Part 4: The Model
In this latest part of my series, I will share what I have learned on selecting a model for Image Classification and how to fine tune that model. I will also show how you can leverage the model to accelerate your labelling process, and finally how to justify your efforts by generating usage and performance […]
Learnings from a Machine Learning Engineer — Part 1: The Data
It is said that in order for a machine learning model to be successful, you need to have good data. While this is true (and pretty much obvious), it is extremely difficult to define, build, and sustain good data. Let me share with you the unique processes that I have learned over several years building […]
➡️ Start Asking Your Data ‘Why?’ — A Gentle Intro To Causality
Correlation does not imply causation. It turns out, however, that with some simple ingenious tricks one can, potentially, unveil causal relationships within standard observational data, without having to resort to expensive randomised control trials. This post is targeted towards anyone making data driven decisions. The main takeaway message is that causality may be possible by […]
Roadmap to Becoming a Data Scientist, Part 4: Advanced Machine Learning
Introduction Data science is undoubtedly one of the most fascinating fields today. Following significant breakthroughs in machine learning about a decade ago, data science has surged in popularity within the tech community. Each year, we witness increasingly powerful tools that once seemed unimaginable. Innovations such as the Transformer architecture, ChatGPT, the Retrieval-Augmented Generation (RAG) framework, and state-of-the-art Computer Vision models — including GANs — have […]
Publish Interactive Data Visualizations for Free with Python and Marimo
Working in Data Science, it can be hard to share insights from complex datasets using only static figures. All the facets that describe the shape and meaning of interesting data are not always captured in a handful of pre-generated figures. While we have powerful technologies available for presenting interactive figures — where a viewer can rotate, filter, […]
Learnings from a Machine Learning Engineer — Part 3: The Evaluation
In this third part of my series, I will explore the evaluation process which is a critical piece that will lead to a cleaner data set and elevate your model performance. We will see the difference between evaluation of a trained model (one not yet in production), and evaluation of a deployed model (one making real-world predictions). In Part 1, […]