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/

Shopping cart sequence learning explained with emojis

https://tech.instacart.com/deep-learning-with-emojis-not-math-660ba1ad6cdc

#deeplearning #medium

Unsupervised sentiment neuron which learns an excellent representation of sentiment, despite being trained only to predict the next character in the text of Amazon reviews.


https://blog.openai.com/unsupervised-sentiment-neuron/

#nlp #sentiment #openai

Karpathy on ML trends for last 5 years:

https://medium.com/@karpathy/a-peek-at-trends-in-machine-learning-ab8a1085a106

#meta #arxiv

OpenAI released $5,000 grant announcement. You can recieve a grant if you develop open source AI project.

https://nat.org/aigrant-get-5-000-for-your-open-source-ai-project-1118dd7db083?gi=f68a512e1c1

#grant #openai

Google released a tool for drawing search, which is a nice attempt to get some hype from recent pix2pix architecture demo.

Basically now you can draw a doodle and let Google search for similar pictures.

https://www.blog.google/topics/machine-learning/fast-drawing-everyone/

Nice post about how you can start solving ML tasks:

Leaf Classification Competition: 1st Place Winner's Interview, Ivan Sosnovik

http://blog.kaggle.com/2017/03/24/leaf-classification-competition-1st-place-winners-interview-ivan-sosnovik/

#kaggle #ml #interview #novice2master

T-SNE visualization by Vadim Markovtsev

Jonker-Volgenant Algorithm + t-SNE = Super Powers: https://blog.sourced.tech/post/lapjv/

#tsne #visualization

Google has set up a new milestone for speech generation: "Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model"

You can listen to generated samples at: https://google.github.io/tacotron/

Paper: https://arxiv.org/abs/1703.10135

#audio #arxiv #google #breakthrough #generative

The Verge’s piece on how Alibaba uses AI to study playing Starcraft. Probably this is the way to attract new brilliant researches into the company, netherless, results look really promising.

http://www.theverge.com/2017/4/3/15164490/alibaba-ai-starcraft-combat

#reinforcementlearning #alibaba #starcraft