Writings
View as Markdown Below you can find my writings on machine learning, data science, and technology. Enjoy!-
Machine Learning Deployment:
Return Actions, Not Scores
A poorly designed machine learning model API will leave you trapped. Properly hiding your implementation will make life much easier! -
Claude Solves SAT Analogies
Word2Vec failed to solve SAT analogies, can modern language models do better? A small test of Anthropic's Claude LLM. -
When Are Large Language Models Useful?
Large language models (LLMs) are incredibly valuable tools, but they're not for everything. Here's a simple rule to know when to use them and when to avoid them. -
How I Write with ChatGPT
OpenAI's ChatGPT is viewed as entertaining but not useful because it makes up facts. But I find it incredibly valuable for writing. Here is how I use it. -
AI, Artists, and Technology
AI generated art took off with the open-source release of Stable Diffusion, leaving some artists worried. As an artist and machine learning engineer, here is my take. -
SWITRS: Pedestrian Safety on Halloween
Halloween can be a dangerous time for pedestrians. In this post, I explore the statistics on pedestrian-vehicle collisions, including when these incidents are most likely to occur. -
SWITRS: On What Days Do Drivers Hit Pedestrians?
Being a pedestrian is dangerous in a world built for automobiles. In this post explore how pedestrian-involved collisions have trended in time. Take a look! -
Using Scikit-learn Pipelines with Pandas Dataframes
Pandas and scikit-learn are two important libraries for building machine learning models. Here is how to get them to work together. -
Computing Machine Learning Features in Real-time
Models often derive great value from real-time features, but computing them is hard because it has to be done quickly. Here is one way I have done it successfully. -
Plotting the 2022 Tour de France
The 2022 Tour de France saw a new winner, Jonas Vingegaard! See how he won in this post!