List of COVID-19 Resources for Machine Learning and Data Science Research
@ArtificialIntelligenceArticles
https://www.marktechpost.com/2020/04/12/list-of-covid-19-resources-for-machine-learning-and-data-science-research/

One knowledge that all DataScience person needs daily.

https://bit.ly/2K8XJA9

A new path for describing the fundamental theory of physics, by Wolfram et al. The end point could be one of mankind's largest intellectual accomplishments.

1) Explanation by Professor Wolfram (Inventor of Wolfram Language, and recipient of MacArthur Grant at only age 21):

https://writings.stephenwolfram.com/2020/04/finally-we-may-have-a-path-to-the-fundamental-theory-of-physics-and-its-beautiful/

2) The Wolfram Fundamental Physics Project page:

https://www.wolframphysics.org/

3) Registry of Notable Universe Models (one of which may turn out to represent our universe):

https://www.wolframphysics.org/universes/

AI v Covid-19
Jürgen Schmidhuber : http://people.idsia.ch/~juergen/ai-covid.html
#ArtificialIntelligence #Covid19 #Covid19Response

COVID-19 Anti-Viral Cure Using Deep Reinforcement Learning
Ifiok Charles : https://github.com/Ifiokcharles/COVID-19-Anti-viral-cure-using-deep-reinforcement-learning
#Covid19 #DeepLearning #ReinforcementLearning

Building a Powerful DQN in TensorFlow 2.0 (explanation & tutorial)
Sebastian Theiler: https://medium.com/analytics-vidhya/building-a-powerful-dqn-in-tensorflow-2-0-explanation-tutorial-d48ea8f3177a
#ReinforcementLearning #MachineLearning #Python #TensorFlow

Monte Carlo Sampling using Langevin Dynamics
I wrote an article on the basics of Langevin Monte Carlo techniques. Please let me know if you find any errors.

Code: https://github.com/abdulfatir/langevin-monte-carlo
Visualization: https://www.youtube.com/watch?v=cVn0kru3hL8

I hope it's helpful for someone. :)

http://abdulfatir.com/Langevin-Monte-Carlo/

Google’s Dataset Search
"Dataset Search has indexed almost 25 million of these datasets, giving you a single place to search for datasets & find links to where the data is.” — Natasha Noy
https://datasetsearch.research.google.com
#ArtificialIntelligence #Datasets #MachineLearning

"Jack London wrote 1,000 words every day before talking to anybody. He was totally, “Let me alone until I’ve got my thousand words!” Then he would drink or proofread the rest of the day. No, my scheduling principle is to do the thing I hate most on my to-do list. By week’s end, I’m very happy....

A person’s success in life is determined by having a high minimum, not a high maximum. If you can do something really well but there are other things at which you’re failing, the latter will hold you back. But if almost everything you do is up there, then you’ve got a good life. And so I try to learn how to get through things that others find unpleasant."


https://www.quantamagazine.org/computer-scientist-donald-knuth-cant-stop-telling-stories-20200416/

Got data and wonder if there's a formula describing it? There's a new physics-inspired AI Feynman algorithm, published today. It automates what took Kepler 4 years.
v/@tegmark
https://bit.ly/3esOWH3
"A core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression
that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions
of practical interest often exhibit symmetries, separability, compositionality, and other simplifying properties.
In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network
fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics,
and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult physicsbased test set, we improve the state-of-the-art success rate from 15 to 90%"

A great course on Artificial Intelligence and Data Science from one of the best universities in the world.

Lecture 1 - Welcome | Stanford CS229: Machine Learning (Autumn 2018) https://www.youtube.com/watch?v=jGwO_UgTS7I

Capsule Networks -- A Probabilistic Perspective
Smith et al.: https://arxiv.org/abs/2004.03553
#ArtificialIntelligence #CapsuleNetworks #MachineLearning

Detecto — An object detection library for PyTorch
https://medium.com/pytorch/detecto-build-and-train-object-detection-models-with-pytorch-5f31b68a8109

We recently started to write an article review series on Generative Adversarial Networks focused on Computer Vision applications primarily. As I think that there isn't a complete overview on the field anywhere online ( at least I haven't found anything yet), I thought that it would be very helpful for many to gather the most important papers on a couple of articles, accumulated years of reading and research in a single resource.

You can find the first 2 parts below:

https://theaisummer.com/gan-computer-vision/

https://theaisummer.com/gan-computer-vision-object-generation/