The natural language decathlon benchmark to do well on ten different nlp tasks including question answering, translation, summarization, sentiment analysis by Salesforce research team.

Project website: https://einstein.ai/research/the-natural-language-decathlon
PDF link: https://einstein.ai/static/images/pages/research/decaNLP/decaNLP.pdf
Github: https://github.com/salesforce/decaNLP

#nlp #dl

Microsoft Research announced Open Data project! This single, cloud-hosted location offers datasets representing many years of data curation and research efforts by Microsoft.

https://www.microsoft.com/en-us/research/blog/announcing-microsoft-research-open-data-datasets-by-microsoft-research-now-available-in-the-cloud/

#data #microsoft #opensource

Kaggle №1 Winning Approach for Plant Seedlings Classification


https://medium.com/@shridhar743/kaggle-1-winning-approach-for-image-classification-challenge-9c1188157a86

Results of the OpenAI's competition on reinforcement learning:
https://blog.openai.com/first-retro-contest-retrospective/

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Translating music to predict a musician’s body movements

Long story short: animating personalized avatar with the help of neural networks.

https://research.fb.com/translating-music-to-predict-a-musicians-body-movements/

#lstm #cvpr2018 #facebook

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This is a day to remembered. #OpenAI 's team of five neural networks, OpenAI Five, has started to defeat amateur human teams (including a semi-pro team) at Dota 2:
https://blog.openai.com/openai-five/

It is important, because Dota2 is a way more complicated game than Chess or Go, where #AI has already surpassed human players.

#rl #reinforcementlearning #dl #dota2 #lstm

Tensorflow: The Confusing Parts (1)

The tutorial for beginners by Jacob, Google AI Resident. This can be nice intro for those, who wanted to get familiar with #TF

This is thorough introduction to the concepts underlying Tensorflow’s API; such as nodes, graphs and sessions.

https://jacobbuckman.com/post/tensorflow-the-confusing-parts-1/?utm_source=telegram&utm_medium=opendatascience

#tensorflow #tutorial #novice #beginner

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Face recognition is now available as a JS-package with the help of face-api.js. It is built on top of #Tensorflow (js version).

https://itnext.io/face-api-js-javascript-api-for-face-recognition-in-the-browser-with-tensorflow-js-bcc2a6c4cf07?gi=a277ad002e2a

#cv #js #tf #dl #cnn

Automated Deep Reinforcement Learning Environment for Hardware of a Modular Legged Robot, from (sic!) Disney Research. They created configurable robots with an "env.reset()" that works on the real hardware enabling popular typical Gym-style ML directly on robots!

Link: https://www.disneyresearch.com/publication/automated-deep-reinforcement-learning-environment-for-hardware-of-a-modular-legged-robot/

#robo #rl #disney

A visual introduction to machine learning.

It is an interactive website, which would be really useful to the beginners, as a perfect visual explanation of how decision trees work. It shows how one can go from statistical parametric evaluation to decision tree building.

Link: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/?utm_source=telegram&utm_medium=opendatascience

#decisiontrees #beginner #novice #firststep #howitworks

Model Tuning and the Bias-Variance Tradeoff
(part II of Visual Introduction to Machine Learning by r2d3)

Bias-Variance tradeoff happens because you have to find optimal balance between model being too simple and too complex. Too complex models tend to overfit — to become to adapted to the training data, so the results on the testing (new, unknown to model) data become less accurate. The article explains with the example from previous part how this actually works.

http://www.r2d3.us/visual-intro-to-machine-learning-part-2/?utm_source=telegram&utm_medium=opendatascience

#decisiontrees #beginner #novice #firststep #howitworks

Post on the estimation of the likelihood of the ads closing due to actually selling the good, which was the goal of Avito’s #Kaggle contest. Author built a #neuralnetwork solution and had 18th place.

https://towardsdatascience.com/kaggle-avito-demand-challenge-18th-place-solution-neural-network-ac19efd6e183

Nice hot start on data streaming with #Spark

https://towardsdatascience.com/learn-how-to-code-and-deploy-machine-learning-models-on-structured-streaming-564749840516

#scala #bigdata #streaming #kafka #tutorial #novice #beginner

Practical Advice for Building Deep Neural Networks

Some practical tips for training deep neural networks based the experiences (rooted mainly in TensorFlow). Some of the suggestions may seem obvious, but they weren’t at some point. Other suggestions may not apply or might even be bad advice for particular task: use discretion!

https://pcc.cs.byu.edu/2017/10/02/practical-advice-for-building-deep-neural-networks/

#neuralnetworks #dl #tensorflow

On the Adam optimizer convergence

Investigation of the convergence of popular optimization algorithms like Adam, RMSProp and propose new variants of these methods which provably converge to optimal solution in convex settings.

Link: https://openreview.net/forum?id=ryQu7f-RZ
PDF: https://openreview.net/pdf?id=ryQu7f-RZ

#iclr2018 #neuralnetworks #optimizers