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.
Can maintaining machine learning in production be easier? I go through some practical tips.
I thought deploying machine learning was hard. Then I had to maintain multiple systems in prod.
What I learned about measuring diversity, novelty, surprise, and serendipity from 10+ papers.
Comparing baselines (matrix factorization) against novel approaches using graphs & NLP.
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).
In-depth sharing on how to put machine learning systems into production.
Don't sell your house to trade algorithmically.
How we built an ML system to predict hospitalization costs at admission; sharing at DATAx Conference.
First, start with the simplest solution, and then add intelligence.
Landing rockets (fun!) via deep Q-Learning (and its variants).
Revisiting the fundamentals and learning new techniques.
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.
How Lazada ranks products to improve customer experience and conversion at Strata 2016.
A simple web app to classify fashion images into Amazon categories.
20 Jun 2015  ·  1 min  ·  [ machinelearning ]
Sharing about my first data science competition at DataScience SG.