learning] [ 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
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.
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...
Crocker's Law, cognitive dissonance, and how to receive (uncomfortable) feedback better.
An expansion of my Twitter thread that went viral.
What I Learnt about evaluating ideas from first-hand participation in a hackathon.
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.
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).
Moving data from one process to another, in a multi-threaded fashion.
You are not your user! Or how to build great products.
A primer on key tech and standards in healthtech though wouldn't recommend it.
Don't sell your house to trade algorithmically.
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).
Revisiting the fundamentals and learning new techniques.
Mostly about learning Java and collaboratively developing an Android app.
Tools and skills to pick up and how to practice them. An Invited Talk with Masters in IT candidates.
Tools and skills to pick up, and how to practice them.
Performing computer vision tasks with ONLY numpy.
Or how to put machine learning models into production.
Cleaning up text and messing with ascii (urgh!)
Got accepted into Georgia Tech's Computer Science Masters!
Parsing json and formatting product titles and categories.
31 Jul 2016  ·  3 min  ·  [ learning ]
Learning Scala from Martin Odersky, father of Scala.