A whirlwind tour of bandits, embedding+MLP, sequences, graph, and user embeddings.
Emphasis on bias, more sequential models & bandits, robust offline evaluation, and recsys in the wild.
Examining the broad strokes of NLP progress and comparing between models
Part II of the previous write-up, this time on applications and frameworks of Spark in production
Sharing my notes & practical knowledge from the conference for people who don't have the time.
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 CS7642 (Reinforcement Learning) - Landing rockets (fun!) via deep Q-Learning (and its variants).
OMSCS CS6476 Computer Vision - Performing computer vision tasks with ONLY numpy.
A web app to find similar products based on image.