Some off-the-beaten uses of Python learned from reading libraries.
Understanding and spotting patterns to use code and components as intended.
Step-by-step walkthrough on the environment, compilers, and installation for ScaNN.
A step-by-step of how to migrate from json comments to Utterances.
Checking for correct implementation, expected learned behaviour, and satisfactory performance.
Updating our FastAPI app to let users select options and download results.
I couldn't find any guides on serving HTML with FastAPI, thus I wrote this to plug the hole on the internet.
I wanted to add my recent writing to my GitHub Profile README but was too lazy to do manual updates.
After this article, we'll have a workflow of tests and checks that run automatically with each git push.
Automate your experimentation workflow to minimize effort and iterate faster.
Beating the baseline using Graph & NLP techniques on PyTorch, AUC improvement of ~21% (Part 2 of 2).
Building a baseline recsys based on data scraped off Amazon. Warning - Lots of charts! (Part 1 of 2).
OMSCS CS7646 (Machine Learning for Trading) - Don't sell your house to trade algorithmically.
OMSCS CS6601 (Artificial Intelligence) - First, start with the simplest solution, and then add intelligence.
OMSCS CS7642 (Reinforcement Learning) - Landing rockets (fun!) via deep Q-Learning (and its variants).
OMSCS CS7641 (Machine Learning) - Revisiting the fundamentals and learning new techniques.
OMSCS CS6476 Computer Vision - Performing computer vision tasks with ONLY numpy.
Or how to put machine learning models into production.
A web app to find similar products based on image.
Cleaning up text and messing with ascii (urgh!)
A simple web app to classify fashion images into Amazon categories.
Parsing json and formatting product titles and categories.