Generation of 3D Art with AI + Project Management in Data Science! 🗿

Generating art with AI is surprisingly easy! Here are 5 links to get you started!

First Edition of Weekly AI Newsletter 2022 🎅

This week I'll be sharing 5 books on Machine Learning that I highly recommend. No particular order, I read all of them and they were truly helpful throughout my PhD!

Cherish Negative Results, They Are a Good Thing | ML Tips #3

I remember when I first started doing research, I always felt bad and stressed whenever I generated a negative results. My thought process was that I must have messed up something along the way and that I wasn't good enough.

Fact Check & Document All Assumptions | ML Tips #2

All assumptions, especially those underlying the project creation, should be documented and fact checked!

Understand Why Your Data Science Project Exist: ML Tips #1

I've never seen a research / data science project that went exactly as planned upfront. Projects either change incrementally, with big pivots or fails.

Reproducing my own research paper after a year and a half of publishing!

A year and a half ago I've published a paper using some code I've created! In this blog post I've checked out if I would be able to reproduce it easily!

The critical topic they don't teach at tech bootcamps

TLDR; navigating layer of abstraction with the least amount of mental overhead is not taught, it should.

How to Leverage Emotions to Solve Root Cause Issues

For some reason, one type of help I've always been very good at providing is solving emotionally charged issues. I've drafted out the framework I'm using to tackle these so that others can get inspired from it!

How much should you document your own tasks?

I had a wonderful question asked to me today from one of my engineer about how much information should be documented in a task assigned to yourself.