How many times have you heard someone say they are data-driven or data centric or that “data is the heart of everything they do”? I’ve…
Continue reading → Why You’re Not as Data-Driven as You Think You Are
Introduction I have a startling admission to make. When I was a student, I scoffed at Dijkstra's algorithm - I had paid it no mind…
Introduction Recently, I've had a chance to play with word embedding models. Word embedding models involve taking a text corpus and generating vector representations for…
Although data science as a job function is relatively new compared to roles like software engineer or database administrator, in the age of “Big Data”,…
Once you’ve built a machine learning classifier, the next step is to validate it and see how well it fits the data. This short post…
The Naive Bayes Algorithm is a simple and elegant approach for tackling supervised learning problems in Machine Learning. This post will be a brief introduction…
Continue reading → Naive and Proud: Introducing the Naive Bayes Algorithm
If you’re like me, you’ve heard a lot about Gradient Descent. You’ve heard that it is a foundational algorithm for optimising functions which all self…
Continue reading → Gradient Descent: The Workhorse of Machine Learning
Job searching can be irritating at best and hopelessly frustrating at worst. This is particularly true in Data Science where the field is still relatively…
Recently I was thinking about the gradient descent algorithm and I was bothered was one question - Why do we go in the direction of…
Introduction Recently, Quanta magazine published an article about how a new basic identity about Linear Algebra has surfaced from Applied Physics. The identity is about…
Continue reading → Eigenvectors from Eigenvalues – Application