ArtificialIntelligenceArticles
Exercism Code practice and mentorship for everyone! Level up your programming skills with 2,659 exercises across 48 languages, and insightful discussion with our dedicated team of welcoming mentors. Exercism is 100% free forever. https://exercism.io/
Want to practice coding for interviews at a company like Google? May I recommend: https://rosalind.info/problems/list-view/, https://projecteuler.net, and https://exercism.io.
projecteuler.net
About - Project Euler
A website dedicated to the fascinating world of mathematics and programming
Facebook has a neural network that can do advanced math
https://www.technologyreview.com/s/614929/facebook-has-a-neural-network-that-can-do-advanced-math/#
https://www.technologyreview.com/s/614929/facebook-has-a-neural-network-that-can-do-advanced-math/#
MIT Technology Review
Facebook has a neural network that can do advanced math
Here’s a challenge for the mathematically inclined among you. Solve the following differential equation for y: You have 30 seconds. Quick! No dallying. The answer, of course, is: If you were unable to find a solution, don’t feel too bad. This expression…
A new deep learning algorithm can predict those at risk of psychosis with 93% accuracy by examining the latent semantic content of an individual’s speech.
https://neurosciencenews.com/ai-psychosis-words-14236/
https://neurosciencenews.com/ai-psychosis-words-14236/
Neuroscience News
The whisper of schizophrenia: Machine learning finds ‘sound’ words predict psychosis
A new deep learning algorithm can predict those at risk of psychosis with 93% accuracy by examining the latent semantic content of an individual's speech.
ArtificialIntelligenceArticles
A new deep learning algorithm can predict those at risk of psychosis with 93% accuracy by examining the latent semantic content of an individual’s speech. https://neurosciencenews.com/ai-psychosis-words-14236/
Here's the original article if you want to know what it actually says. https://www.nature.com/articles/s41537-019-0077-9
Nature
A machine learning approach to predicting psychosis using semantic density and latent content analysis
Schizophrenia - A machine learning approach to predicting psychosis using semantic density and latent content analysis
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/
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/
YouTube
Deep Learning Basics: Introduction and Overview
An introductory lecture for MIT course 6.S094 on the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers. For more lecture videos on deep…
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
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
de Pedro Torres Perez
https://rsci.app.link/LczXR6YnO2?_p=c11731dc9a0660eee31c8de3e9b6b9
Medium
AI: Monte Carlo Tree Search (MCTS)
Monte Carlo Tree search is a fancy name for one Artificial Intelligence algorithm used specially in games. Alpha Go reportedly used this…
CS 188 : Introduction to Artificial Intelligence
Pieter Abbeel & Dan Klein, University of California, Berkeley : https://inst.eecs.berkeley.edu/~cs188/fa18/
#ArtificialIntelligence #DeepLearning #ReinforcementLearning
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
Zhang et al.: https://arxiv.org/abs/1911.05954
#ArtificialIntelligence #Graph #NeuralNetworks
arXiv.org
Hierarchical Graph Pooling with Structure Learning
Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured data, have drawn considerable attention and achieved state-of-the-art performance in numerous graph related...
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
White et al.: https://arxiv.org/abs/1910.11858
#Bayesian #Optimization #NeuralArchitectureSearch
White et al.: https://arxiv.org/abs/1910.11858
#Bayesian #Optimization #NeuralArchitectureSearch
arXiv.org
BANANAS: Bayesian Optimization with Neural Architectures for...
Over the past half-decade, many methods have been considered for neural architecture search (NAS). Bayesian optimization (BO), which has long had success in hyperparameter optimization, has...
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/)
[https://karpathy.github.io/2019/04/25/recipe/](https://karpathy.github.io/2019/04/25/recipe/)
karpathy.github.io
Andrej Karpathy blog
Musings of a Computer Scientist.
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
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
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
By Brian Stallard, Cold Spring Harbor Laboratory : https://phys.org/news/2019-12-machine-gene.html
#ArtificialIntelligence #Genetics #MachineLearning
phys.org
Finally, machine learning interprets gene regulation clearly
In this age of "big data," artificial intelligence (AI) has become a valuable ally for scientists. Machine learning algorithms, for instance, are helping biologists make sense of the dizzying number of ...
"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
CVXPY creates powerful new PyTorch and TensorFlow layers
Agrawal et al.: https://locuslab.github.io/2019-10-28-cvxpylayers/
#PyTorch #TensorFlow #NeurIPS2019
locuslab.github.io
Differentiable Convex Optimization Layers
CVXPY creates powerful new PyTorch and TensorFlow layers
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
Akiba et al.: https://arxiv.org/abs/1907.10902
#ArtificialIntelligence #DataScience #MachineLearning