Congrats to legendary scientists Yoshua Bengio, Yann LeCun and Geoffrey Hinton for receiving Turing Award today at ceremony hosted by ACM.Google Senior Fellow Jeff Dean is handing over this prestigious award to these legends. ACM - Association for Computing Machinery Facebook AI Google AI Google Universite De Montreal - Campus Laval

A Rhythmic Theory of Attention


https://www.sciencedirect.com/science/article/abs/pii/S136466131830281X

Best paper award at #ICML2019 main idea: unsupervised learning of disentangled representations is fundamentally
impossible without inductive biases. Verified theoretically & experimentally. https://arxiv.org/pdf/1811.12359.pdf

TensorFlow 2.0.0-beta0 CPU, Python 3.5, ARMv7 for Raspberry Pi
Code: https://github.com/yaroslavvb/tensorflow-community-wheels/issues/114
#tensorflow #raspberrypi #machinelearning #python3 #artificialintelligence

Apprentissage automatique pour la visualisation

Par Ian Johnson : https://medium.com/@enjalot/machine-learning-for-visualization-927a9dff1cab

#intelligenceartificielle

SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognition With Distractors
https://openaccess.thecvf.com/content_CVPRW_2019/html/WiCV/Jalal_SIDOD_A_Synthetic_Image_Dataset_for_3D_Object_Pose_Recognition_CVPRW_2019_paper.html

Eye Contact Correction using Deep Neural Networks. arxiv.org/abs/1906.05378

Inspired by precocial species in biology, we set out to search for neural net architectures that can already (sort of) perform various tasks even when they use random weight values.

Article: weightagnostic.github.io
PDF: arxiv.org/abs/1906.04358

Generative Modeling and Model Based Reasoning for Robotics and AI Yann LeCun https://www.youtube.com/watch?v=cQvyPNmpFgc https://t.iss.one/ArtificialIntelligenceArticles

AI that learns to write code!

Researchers combine deep learning and symbolic reasoning for a more flexible way of teaching computers to program.

https://www.profillic.com/paper/arxiv:1902.06349

GAN Lab: Play with Generative Adversarial Networks (GANs) in your browser!
By created by Minsuk Kahng, Nikhil Thorat, Polo Chau, Fernanda Viégas, and Martin Wattenberg: https://poloclub.github.io/ganlab/
Research paper: https://minsuk.com/research/papers/kahng-ganlab-vast2018.pdf
#AI #ArtificialIntelligence #GenerativeAdversarialNetworks

Build your own robotic cat!
Blog by Alex Bate: https://www.raspberrypi.org/blog/robotic-cat-petoi-nybble/
#Arduino #ArtificialIntelligence #MachineLearning #RaspberryPi #Robotics

TensorSpace is a neural network 3D visualization framework
Built on TensorFlow.js, Three.js and Tween.js: https://tensorspace.org
#NeuralNetworks #ArtificialInteligence #DeepLearning #MachineLearning #Visualization

Machine learning top 10 articles for the past month (v.June 2019)

https://medium.com/@Mybridge/machine-learning-top-10-articles-for-the-past-month-v-june-2019-be8235cabaf0 https://t.iss.one/ArtificialIntelligenceArticles

Spinning Up in Deep RL
By Joshua Achiam: https://openai.com/blog/spinning-up-in-deep-rl/
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

Simon et al.: https://arxiv.org/abs/1803.06199

#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PatternRecognition

The Best of AI: New Articles Published This Month (May 2019)
https://blog.sicara.com/05-2019-best-ai-new-articles-this-month-4a202e173e5a

Speech Model Pre-training for End-to-End Spoken Language Understanding

Lugosch et al.: https://arxiv.org/abs/1904.03670

#spokenlanguageunderstanding #machinelearning #technology

Python Machine Learning (2nd Ed.) Code Repository

By Sebastian Raschka et al.: https://github.com/rasbt/python-machine-learning-book-2nd-edition

#100DaysOfMLCode #Clustering #DataScience #DeepLearning #MachineLearning #NeuralNetworks #Programming #SupervisedLearning

"Neural Networks for Machine Learning by Geoffrey Hinton" (Coursera 2013)

Video Lectures: https://www.youtube.com/watch?v=2fRnHVVLf1Y&list=PLiPvV5TNogxKKwvKb1RKwkq2hm7ZvpHz0

#100DaysOfMLCode #ArtificialIntelligence #DeepLearning #NeuralNetworks #MachineLearning

Data Pre-processing for Deep Learning models (Deep Learning with Keras – Part 2)
Training deep learning models requires data… A lot of data! Unfortunately, in most cases data comes messy, and our models are very sensitive towards this. Therefore, we need to be careful while preparing our data to achieve the best results.

Part 1- https://www.marktechpost.com/2019/06/11/deep-learning-with-keras-tutorial-part-1/

Part 2- https://www.marktechpost.com/2019/06/14/deep-learning-wit...
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