79 posts, 125,794 words, innumerable hours. Opinions & bad jokes my own. Give feedback 🎁[ datascience learning machinelearning python career productivity omscs production engineering lazada til business leadership communication agile recsys misc spark nlp deeplearning ]
For years I've refined my routines and found tools to manage my time. Here I share it with readers.
My tools for organization and creation, autopilot routines, and Maker's schedule
A step-by-step of how to migrate from json comments to Utterances.
Checking for correct implementation, expected learned behaviour, and satisfactory performance.
Why read papers, what papers to read, and how to read them.
How not to become an expert beginner and to progress through beginner, intermediate, and so on.
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
Updating our FastAPI app to let users select options and download results.
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.
Ever revisit a project & replicate the results the first time round? Me neither. Thus I adopted these habits.
It's not enough to have a good strategy and plan. Execution is just as important.
I wanted to add my recent writing to my GitHub Profile README but was too lazy to do manual updates.
I thought giving it my all led to maximum outcomes; then I learnt about the 85% rule.
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.
After this article, we'll have a workflow of tests and checks that run automatically with each git push.
A curious discussion made me realize my expert blind spot. And no, Airflow is not late.
Haste makes waste. Diving into a data science problem may not be the fastest route to getting it done.
Initially, I didn't like it. But over time, it grew on me. Here's why.
Crocker's Law, cognitive dissonance, and how to receive (uncomfortable) feedback better.
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.
An expansion of my Twitter thread that went viral.
What I Learnt about evaluating ideas from first-hand participation in a hackathon.
What I learned about measuring diversity, novelty, surprise, and serendipity from 10+ papers.
Why you should give a talk and some tips from five years of speaking and hosting meet-ups.
12 Apr 2020  ·  6 min  ·  [ career ]
Should I join a start-up? Which offer should I accept? A simple metaphor to guide your decisions.
Using a Zettelkasten helps you make connections between notes, improving learning and memory.
Writing begins before actually writing; it's a cycle of reading -> note-taking -> writing.
Automate your experimentation workflow to minimize effort and iterate faster.
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).
Building a baseline recsys based on data scraped off Amazon. Warning - Lots of charts! (Part 1 of 2).
Moving data from one process to another, in a multi-threaded fashion.
You are not your user! Or how to build great products.
25 Aug 2019  ·  1 min  ·  [ misc ]
Moving off wordpress and hosting for free on GitHub. And gaining full customization!
A primer on key tech and standards in healthtech though wouldn't recommend it.
Don't sell your house to trade algorithmically.
No, you don't need a PhD or 10+ years of experience.
Taking the best from agile and modifying it to fit the data science process (Part 2 of 2).
A deeper look into the strengths and weaknesses of Agile in Data Science projects (Part 1 of 2).
First, start with the simplest solution, and then add intelligence.
Figuring out how to scale education widely through technology.
Landing rockets (fun!) via deep Q-Learning (and its variants).
Culture >> Hierarchy, Process, Bureaucracy.
Revisiting the fundamentals and learning new techniques.
How being a Lead / Manager is different from being an individual contributor.
Mostly about learning Java and collaboratively developing an Android app.
Tools and skills to pick up, and how to practice them.
Performing computer vision tasks with ONLY numpy.
If things are not failing, you're not innovating enough. - Elon Musk
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.
Got accepted into Georgia Tech's Computer Science Masters!
23 Oct 2016  ·  4 min  ·  [ misc ]
A card sorting game to discover youl passion by identifying skills you like and dislike.
Parsing json and formatting product titles and categories.
31 Jul 2016  ·  4 min  ·  [ learning ]
Learning Scala from Martin Odersky, father of Scala.
06 Jul 2016  ·  1 min  ·  [ misc ]
Time to start writing.
17 Sep 2015  ·  8 min  ·  [ datascience ]
Guest post of how DataKind SG worked with NGOs to frame their problems and suggests solutions
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Hey there. Didn’t expect anyone back here; this is where I started writing. What do I write about? Why do I write? Answers to these and more in the FAQ.
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I write about how to be effective in data science, learning, and career. Get weekly updates.
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