The End of the First Phase of My Data Science Journey The completion of my Capstone marks the last data science course in my Master’s in Health Data Science program. I have one more semester before I graduate, but the courses I have left are health research and misc. Effectively…

M.S. HDS Capstone Complete For the last couple of months I’ve been too busy to write about the things I’ve been learning and doing, but if you’re curious (or have a previously incurable case of insomnia) click this link to access my just completed Capstone project for my Master’s in…

Recognizing Rewards and Risks of Redeploying Prejudiced Pretrained Ptransformers In a couple of previous posts I’ve talked about the level of data crunching that has to happen in order to train a Transformer Large Language Model (LLM). This turns out to be an…

Demystifying the Magic Behind Large Language Models When I first started reading/watching people in the field talking about complex encoding and decoding systems for NLP (including transformers and various other neural network architectures), if I’m being honest, it sounded (still sounds?) like magic. …

An Initial Foray into Bloom using Google Colab As part of my Master’s program Capstone project, I get to explore how to implement Large Language Models (LLMs). …

When Overthinking Produces the Simplest Explanation In my previous post on the Monty Hall Problem, I walked through the logic and statistics behind the 66% probability of winning by switching your answer when given the option to do so. Math, examples, and code stuffs — including examples of tests I…

Making Sense of the Monty Hall Problem The Monty Hall Problem is one of those proof points that illustrates how bad humans (myself included) are at statistical thinking. The quick version is this: you are playing a game with three options, often illustrated as doors. …

The Blurring Line Between Biological and Digital Intelligence If you’re not familiar with the Chinese room thought exercise, here’s the tl;dr — if a computational program can take an input and produce the exact same output that a knowledgeable, typical human might produce, is it actually thinking? …

When Going Faster Slows You Down Usually when building a machine learning algorithm from scratch (like we do in the Cornell Machine Learning program), you want to employ vectorized / matrixed math operations everywhere you possibly can in order to speed up performance. The reason for this is that it’s…