One of the BEST #MachineLearning Glossary by Google
It will definitely come in handy - https://developers.google.com/machine-learning/glossary/
It will definitely come in handy - https://developers.google.com/machine-learning/glossary/
Google for Developers
Machine Learning Glossary | Google for Developers
Stanford Nears All-Optical Artificial Neural Network
https://www.photonics.com/Article.aspx?AID=63684
https://www.photonics.com/Article.aspx?AID=63684
Photonics
Stanford Nears All-Optical Artificial Neural Network
Researchers have shown that it is possible to train artificial neural networks directly on an optical chip. The research demonstrates that an optical
New #deeplearning paper at the intersection of #AI #mathematics #psychology and #neuroscience: A mathematical theory of semantic development in deep neural networks: arxiv.org/abs/1810.10531 https://t.iss.one/ArtificialIntelligenceArticles
How can we guide #AI to learn the way humans do? Scientists are looking at the brain's structure to find out.
Read more in this Perspective from Science. https://science.sciencemag.org/content/363/6428/692
Read more in this Perspective from Science. https://science.sciencemag.org/content/363/6428/692
Deep Learning and Reinforcement Learning Summer School, Toronto 2018
videos:
https://videolectures.net/DLRLsummerschool2018_toronto/
slides:
https://dlrlsummerschool.ca/speaker-slides/
videos:
https://videolectures.net/DLRLsummerschool2018_toronto/
slides:
https://dlrlsummerschool.ca/speaker-slides/
Prof. Patrick Henry Winston introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence.
https://www.newworldai.com/artificial-intelligence-complete-lectures-01-23/
https://www.newworldai.com/artificial-intelligence-complete-lectures-01-23/
New World : Artificial Intelligence
MIT Artificial Intelligence | 23 Lectures | Patrick H. Winston | 2010 - New World : Artificial Intelligence
Prof. Patrick Henry Winston introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence.
Playing Atari with Six Neurons
Cuccu et al.: https://arxiv.org/pdf/1806.01363.pdf
#artificialintelligence #machinelearning #deeplearning #neuralnetworks
Cuccu et al.: https://arxiv.org/pdf/1806.01363.pdf
#artificialintelligence #machinelearning #deeplearning #neuralnetworks
Illustrated Deep Learning cheatsheets covering Stanford's CS 230 class
Set of illustrated Deep Learning cheatsheets covering the content of Stanford's CS 230 class:
Convolutional Neural Networks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks
Recurrent Neural Networks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
Tips and tricks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks
Set of illustrated Deep Learning cheatsheets covering the content of Stanford's CS 230 class:
Convolutional Neural Networks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks
Recurrent Neural Networks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
Tips and tricks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks
stanford.edu
CS 230 - Convolutional Neural Networks Cheatsheet
Teaching page of Shervine Amidi, Graduate Student at Stanford University.
Understanding Neural Networks via Feature Visualization: A survey
Nguyen et al.: https://arxiv.org/pdf/1904.08939v1.pdf
#neuralnetworks #generatornetwork #generativemodels
Nguyen et al.: https://arxiv.org/pdf/1904.08939v1.pdf
#neuralnetworks #generatornetwork #generativemodels
Unsupervised Learning with Graph Neural Networks
By Thomas Kipf.
Slides : https://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
Recording: https://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
#deeplearning #neuralnetworks #unsupervisedlearning #technology
By Thomas Kipf.
Slides : https://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
Recording: https://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
#deeplearning #neuralnetworks #unsupervisedlearning #technology
Path-Augmented Graph Transformer Network
Chen et al.: https://arxiv.org/abs/1905.12712
#ArtificialIntelligence #DeepLearning #MachineLearning
Chen et al.: https://arxiv.org/abs/1905.12712
#ArtificialIntelligence #DeepLearning #MachineLearning
Video from Stills: Lensless Imaging with Rolling Shutter
Antipa et al.: https://arxiv.org/abs/1905.13221v1
#ArtificialIntelligence #DeepLearning #MachineLearning
Antipa et al.: https://arxiv.org/abs/1905.13221v1
#ArtificialIntelligence #DeepLearning #MachineLearning
On Conditioning GANs to Hierarchical Ontologies.) arxiv.org/abs/1905.06586
Important paper from Zellers et al. - "Defending Against Neural Fake News": arxiv.org/abs/1905.12616
Great to see more technical work on this topic, as well as further discussion of appropriate language model publication norms.
Great to see more technical work on this topic, as well as further discussion of appropriate language model publication norms.
arXiv.org
Defending Against Neural Fake News
Recent progress in natural language generation has raised dual-use concerns. While applications like summarization and translation are positive, the underlying technology also might enable...
A Brief Introduction to Machine Learning for Engineers
By Osvaldo Simeone: https://arxiv.org/abs/1709.02840
#ArtificialIntelligence #Engineering #MachineLearning #NeuralNetworks
By Osvaldo Simeone: https://arxiv.org/abs/1709.02840
#ArtificialIntelligence #Engineering #MachineLearning #NeuralNetworks
arXiv.org
A Brief Introduction to Machine Learning for Engineers
This monograph aims at providing an introduction to key concepts, algorithms, and theoretical results in machine learning. The treatment concentrates on probabilistic models for supervised and...
The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial
Benyamin Ghojogh and Mark Crowley : https://arxiv.org/abs/1905.12787
#ArtificialIntelligence #DeepLearning #MachineLearning
Benyamin Ghojogh and Mark Crowley : https://arxiv.org/abs/1905.12787
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
The Theory Behind Overfitting, Cross Validation, Regularization,...
In this tutorial paper, we first define mean squared error, variance, covariance, and bias of both random variables and classification/predictor models. Then, we formulate the true and...
A Guide for Making Black Box Models Explainable
By Christoph Molnar: https://christophm.github.io/interpretable-ml-book/
#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks
By Christoph Molnar: https://christophm.github.io/interpretable-ml-book/
#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks
christophm.github.io
Interpretable Machine Learning
Notes from Karpathy on common mistakes when training NN
https://karpathy.github.io/2019/04/25/recipe/
https://karpathy.github.io/2019/04/25/recipe/
karpathy.github.io
A Recipe for Training Neural Networks
Musings of a Computer Scientist.
CompILE: Compositional Imitation Learning and Execution
Kipf et al.: https://arxiv.org/abs/1812.01483
Code: https://github.com/tkipf/compile
#ArtificialIntelligence #DeepLearning #MachineLearning
Kipf et al.: https://arxiv.org/abs/1812.01483
Code: https://github.com/tkipf/compile
#ArtificialIntelligence #DeepLearning #MachineLearning