3Blue1Brown walks through the mathematics of epidemics through simulations.

The source code for this video is visible here:

https://github.com/3b1b/manim/blob/shaders/from_3b1b/active/sir.py

3Blue1Brown walks through the mathematics of epidemics through simulations.

The source code for this video is visible here:

https://github.com/3b1b/manim/blob/shaders/from_3b1b/active/sir.py

Eddie Woo explains survivor bias in this short video.

In honor or Pi Day, here is Calculus Rhapsody.

For Mathematics, trees are more useful than strings.

Professor Thorsten Altenkirch takes us through a functional approach to coding them in Python.

Zach Star covers the applications of eigenvectors and eigenvalues (in and outside of mathematics) that I definitely didn’t learn in school.

SciShow explores game theory, a branch of mathematics that reinforcement learning relies upon heavily.

More game theory posts.

Zach Star explains the mathematics of image compression – an important technology even in our age of broadband connections.

Siraj Raval has a video exploring a paper about genomics and creating reliable machine learning systems.

Deep learning classifiers make the ladies (and gentlemen) swoon, but they often classify novel data that’s not in the training set incorrectly with high confidence. This has serious real world consequences! In Medicine, this could mean misdiagnosing a patient. In autonomous vehicles, this could mean ignoring a stop sign. Machines are increasingly tasked with making life or death decisions like that, so it’s important that we figure out how to correct this problem! I found a new, relatively obscure yet extremely fascinating paper out of Google Research that tackles this problem head on. In this episode, I’ll explain the work of these researchers, we’ll write some code, do some math, do some visualizations, and by the end I’ll freestyle rap about AI and genomics. I had a lot of fun making this, so I hope you enjoy it!

Likelihood Ratios for Out-of-Distribution Detection paper: https://arxiv.org/pdf/1906.02845.pdf

The researcher’s code: https://github.com/google-research/google-research/tree/master/genomics_ood

Siraj Raval gets back to inspiring people to get into AI and pokes fun at himself.

Almost exactly 4 years ago I decided to dedicate my life to helping educate the world on Artificial Intelligence. There were hardly any resources designed for absolute beginners and the field was dominated by PhDs. In 2020, thanks to the extraordinary contributions of everyone in this community, all that has changed. It’s easier than ever before to enter into this field, even without an IT background. We’ve seen brave entrepreneurs figure out how to deploy this technology to save lives (medical imaging, automated diagnosis) and accelerate Science (AlphaFold). We’ve seen algorithmic advances (deepfakes) and ethical controversies (automated surveillance) that shocked the world. The AI field is now a global, cross-cultural movement that’s not limited to academics alone. And that’s something all of us should be proud of, we’re all apart of this. I’ve packed a lot into this episode! I’ll give my annual lists of the best ML language and libraries to learn this year, how to learn ML in 2020, as well as 8 predictions about where this field is headed. I had a lot of fun making this, so I hope you enjoy it!

3Blue1Brown explores perhaps the most important formula in probability: Bayes Theorem.

The study with Steve:

## Comments

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