Learning Machine Learning
A lot of people want to learn machine learning these days. But the daunting bottom-up curriculum that most ML teachers propose is enough discourage a lot of newcomers.
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005 📧 - Are AIs model becoming sentient?
AI's are making waves once again fueled by consciousness claim hype. Before we head into another AI winter, let's try to reduce the hype (just enough to still get funding and calm everyone expectation).
004 📧 - Gradient Descent Simply Explained
Some concepts in machine learning may at first glance look extremely opaque and difficult to grasp. Yet, most are pretty accessible and simple.
However, they are usually clouded by terminology you don't understand or heavy math notations. When you are faced with one such concept that is stumping you:
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Data Science Tips: Incorporate Code Review in All your Projects
To ensure high quality data science project are produced by your team, incorporating code reviews in your workflow is a must!
Shannon Entropy from Theory to Python
Being able to quantify the amount of information in a sequence is important in many fields and applies to many data set.
Shannon Entropy is one such information theory method that given a random variable and historic about this variable occurrence can quantify the average level of information!
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.