61 posts, 85,526 words since 2016-07-06. Opinions & bad jokes my own. Have feedback? Reach out![ datascience learning machinelearning python production career omscs project productivity lazada engineering business agile recsys misc leadership writing spark reading notetaking speaking ]
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  ·  5 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  ·  3 min  ·  [ project ]
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  ·  3 min  ·  [ learning ]
Learning Scala from Martin Odersky, father of Scala.
06 Jul 2016  ·  1 min  ·  [ misc ]
Time to start writing.
• • •
Hey there. Didn’t expect anyone back here; this is where I started writing.
I started writing because my mentors said communication’s the most important skill for a data scientist. I didn’t buy it… but decided to try anyway. I discovered that writing helps to share knowledge scalably—just publish online. (No more repeating myself.)
is difficult. I think I know enough to write; but when I write, I realise how little I know. Writing
is thinking and
learning. And when I publish this thinking process, like-minded people reach out and we become
friends. Now, it's become a habit.
• • •
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