GAN architecture provides extremely live-looking generated pictures.

https://medium.com/@Moscow25/gans-will-change-the-world-7ed6ae8515ca#.pskqd0wjw

There is a new $1MM competition on Kaggle to use ML / AI to diagnose lung cancer from CT scans.

Not only it is the great breakthrough for Kaggle (it is the first competition with this huge prize fund), it is also a breakthrough for science, since top world researchers and enginners will compete to basically crowdsource and ease the lung cancer diagnostics.

Competition is available at: https://www.kaggle.com/c/data-science-bowl-2017

#kaggle #segmentation #deeplearning #cv

Trained agent was able to beat some humans in one-on-one no-limit Texas Holdem. It is not "beating human" yet.

https://www.technologyreview.com/s/603342/poker-is-the-latest-game-to-fold-against-artificial-intelligence/

New release in PyTorch: «GPU Tensors, Dynamic Neural Networks and deep Python integration. Hello world!»

http://pytorch.org

Deep Learning Pipeline for Alzheimer’s Disease Prediction

https://devblogs.nvidia.com/parallelforall/nvidia-digits-alzheimers-disease-prediction/

#deeplearning #digits

Image-to-Image Translation in Tensorflow

http://affinelayer.com/pix2pix/index.html

#deeplearning #tf #dl

Today Kaggle announced the launch of Two Sigma's new recruiting competition. In this competition, participants are invited to explore detailed NYC rental listing data from Two Sigma's competition co-sponsor, RentHop, to ease the often hectic process of finding the perfect home.

#kaggle

If you have any news worth spreading, please address @opendatasciencebot and send him a link with quick description.

Simple/limited/incomplete benchmark for scalability, speed and accuracy of machine learning libraries for classification.

https://github.com/szilard/benchm-ml

#github #opensource #worthspreading

2.72% on the CIFAR-10

https://github.com/xgastaldi/shake-shake

#github #cifar

Best probability visualisations ever. If you struggling with basic Probability Theory concepts, or just want to review them, this site is the best for understanding/refreshing.

http://students.brown.edu/seeing-theory/

https://www.paperspace.com/ml

Nice ML computing in the cloud

Another on-demand-GPU cloud solution:

http://floydhub.com

https://github.com/soulmachine/machine-learning-cheat-sheet

#github #cheatsheet

An article about using #cv to predict car manufacturer, political views and average household based on Google's Street View

https://arxiv.org/pdf/1702.06683.pdf

Deep Image Matting

Image background segmentation.

https://arxiv.org/pdf/1703.03872.pdf

#cv #deeplearning

Visual aesthetics are very personal, often subconscious, and hard to express.  In a world with an overload of photographic content, a lot of time and effort is spent manually curating photographs, and it’s often hard to separate the good images from the visual noise. The question we put forward at EyeEm is: can a machine learn personalized aesthetics embodied in a set of chosen photos, and recreate them in a different set?


https://devblogs.nvidia.com/parallelforall/personalized-aesthetics-machine-learning/

#cv #deeplearning

Ruslan Salakhutdinov, Apple AI leader, thoughts on deep learning:

https://www.technologyreview.com/s/603912/apples-ai-director-heres-how-to-supercharge-deep-learning/

#apple #deeplearning #theory

Google came up with special TPU (Tensor Processing Unit). So we can expect more efficient solutions, involving AI.

https://www.wired.com/2017/04/building-ai-chip-saved-google-building-dozen-new-data-centers/