MIT lecture series on deep learning in 2019
MIT lecture series on deep learning:Basics: https://www.youtube.com/watch?v=O5xeyoRL95U&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf

MIT lecture series on deep learning: State of the Art:https://www.youtube.com/watch?v=53YvP6gdD7U&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf

MIT lecture series on deep learning: Introduction to Deep RL: https://www.youtube.com/watch?v=zR11FLZ-O9M&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf

Find The Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python Programming Resources
https://www.marktechpost.com/free-resources/

Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
Grathwohl et al.: https://arxiv.org/abs/1912.03263
#ArtificialIntelligence #DeepLearning #MachineLearning

“AI: Monte Carlo Tree Search (MCTS)”

de Pedro Torres Perez

https://rsci.app.link/LczXR6YnO2?_p=c11731dc9a0660eee31c8de3e9b6b9

CS 188 : Introduction to Artificial Intelligence
Pieter Abbeel & Dan Klein, University of California, Berkeley : https://inst.eecs.berkeley.edu/~cs188/fa18/
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

Hierarchical Graph Pooling with Structure Learning
Zhang et al.: https://arxiv.org/abs/1911.05954
#ArtificialIntelligence #Graph #NeuralNetworks

BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
White et al.: https://arxiv.org/abs/1910.11858
#Bayesian #Optimization #NeuralArchitectureSearch

A Recipe for Training Neural Networks : [Andrej Karpathy blog](https://karpathy.github.io/)

[https://karpathy.github.io/2019/04/25/recipe/](https://karpathy.github.io/2019/04/25/recipe/)

Generating Interactive Worlds with Text
we present a machine learning (ML) approach to creating a cohesive and interesting world built from elements of the text-based fantasy game
ML models were trained to play the game by mimicking the actions and dialogues of human players in fixed settings built by crowd-workers.
In contrast, in this work, we study models for assembling the game itself rather than agents that play it
Paper:
https://arxiv.org/pdf/1911.09194.pdf

On the Morality of Artificial Intelligence
Alexandra Luccioni, Yoshua Bengio : https://arxiv.org/abs/1912.11945
#Society #AIEthics #ArtificialIntelligence

"Finally, machine learning interprets gene regulation clearly"
By Brian Stallard, Cold Spring Harbor Laboratory : https://phys.org/news/2019-12-machine-gene.html
#ArtificialIntelligence #Genetics #MachineLearning

"Differentiable Convex Optimization Layers"
CVXPY creates powerful new PyTorch and TensorFlow layers
Agrawal et al.: https://locuslab.github.io/2019-10-28-cvxpylayers/
#PyTorch #TensorFlow #NeurIPS2019

deeptraffic: DeepTraffic is a deep reinforcement learning competition, part of the MIT Deep Learning series. https://github.com/lexfridman/deeptraffic

"Optuna: A Next-generation Hyperparameter Optimization Framework"
Akiba et al.: https://arxiv.org/abs/1907.10902
#ArtificialIntelligence #DataScience #MachineLearning

Neural-Symbolic Cognitive Reasoning
Authors: D'Avila Garcez, Artur S., Lamb, Luís C., Gabbay, Dov M - https://www.springer.com/gp/book/9783540732457

Deep learning model for breast cancer detection beats five full-time radiologists and previous SOTA models from NYU and MIT
Paper: https://arxiv.org/pdf/1912.11027.pdf

Deep learning model for breast cancer detection beats five full-time radiologists and previous SOTA models from NYU and MIT

Deep learning approach improves accuracy of screening mammography
https://arxiv.org/abs/1912.11027

This story was told by the American inventor/entrepreneur/scientist Daniel Hillis, who did pioneering work in the application of parallel computers to artificial intelligence.

Richard Feynman's son, Carl, was working as an undergraduate assistant to Hillis in a project they called "Connection Machine." Feynman, which was interested in computing since Los Alamos, was following the project closely. One day after having lunch with Hillis, Feynman agreed to work at his startup company in the summer. Shortly after the company was incorporated, Feynman showed up at the headquarters, saying, "Richard Feynman reporting for duty. OK, boss, what's my assignment?" Everybody in the company was surprised by the unexpected situation, and after some discussion, they gave Feynman the assignment of advising "on the application of parallel processing to scientific problems." Feynman said: "That sounds like a bunch of baloney. Give me something real to do." So they sent him out to buy office supplies, which he promptly did.