I work at the intersection of consumer data & tech to help customers via machine learning systems. I write about effective data science, learning, and career to help readers.
Just released: How To Be An Effective Data Scientist 🚀, a free email course.
Surprising lessons I picked up from the best books, essays, and videos on writing non-fiction.
Why OMSCS? How can I get accepted? How much time needed? Did it help your career? And more...
I couldn't find any guides on serving HTML with FastAPI, thus I wrote this to plug the hole on the internet.
After this article, we'll have a workflow of tests and checks that run automatically with each git push.
Initially, I didn't like it. But over time, it grew on me. Here's why.
Can maintaining machine learning in production be easier? I go through some practical tips.
Using a Zettelkasten helps you make connections between notes, improving learning and memory.
How hard work, many failures, and a bit of luck got me into the field and up the ladder.
Beating the baseline using Graph & NLP techniques on PyTorch, AUC improvement of ~21% (Part 2 of 2).
A deeper look into the strengths and weaknesses of Agile in Data Science projects (Part 1 of 2).