Access, serving, integrity, convenience, autopilot; use what you need.
Design and architecture, tech stack, methodology, results, and lessons learned.
Why real-time? How have China & US companies built them? How to design & build an MVP?
Data cleaning, transfer learning, overfitting, ensembling, and more.
A personal take on their deliverables and skills, and what it means for the industry and your team.
Setbacks she faced, overcoming them, and how writing changed her life.
Step-by-step walkthrough on the environment, compilers, and installation for ScaNN.
Emphasis on bias, more sequential models & bandits, robust offline evaluation, and recsys in the wild.
Checking for correct implementation, expected learned behaviour, and satisfactory performance.
Examining the broad strokes of NLP progress and comparing between models
Why (and why not) be more end-to-end, how to, and Stitch Fix and Netflix's experience
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.
OMSCS CS7646 (Machine Learning for Trading) - Don't sell your house to trade algorithmically.
How we built an ML system to predict hospitalization costs at admission; sharing at DATAx Conference.
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!)
How Lazada ranks products to improve customer experience and conversion at Strata 2016.
A simple web app to classify fashion images into Amazon categories.
Sharing about my first data science competition at DataScience SG.
20 Jun 2015  ·  1 min  ·  machinelearning