Data Science by ODS.ai 🦜
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First Telegram Data Science channel. Covering all technical and popular staff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. To reach editors contact: @malev
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#OpenDataScience community (ods.ai) recently released Open Machine Learning Course. This is a community-driven course, covering #production, #Kaggle (actually #CompetitiveDataScience, but we use this hashtag for the first time), #DL, #RL, #ML and validated on the russian-speaking DS community, which was translated into english.

There are lots of assignments and some competitions during the course. Interactive rating highly motivates and makes it fun to participate.

Next session starts on October 1. Welcome!

Link: https://mlcourse.ai?utm_source=telegram&utm_medium=opendatascience
You can also suggest any news, repo or post to our channel with @opendatasciencebot

You are welcome to share your open source work to request help, or just ask for starring it.

You are welcome to propose any post or article you want to.

Channel editors however will decide if that follows channel policy, which is OK for the majority of subscribers, judging by recent poll.

Thank you for your patience and constant forwards to friends! You do really matter, dear subscribers and the organic growth of this channel is the best reward.
Data Hackers is a brazilian data science community and we've just released the first episode of our podcast. They reached out with the @opendatasciencebot saying that there're a lot of brazilians in our channel and it would be great to share this Portugese message:

Lançamos o podcast do Data Hackers. O primeiro episódio já está disponível no Spotify, Google Podcasts, iTunes, e muitas outras plataformas de streaming. Você também pode ouvir no post abaixo, além de saber um pouco mais sobre o projeto.

https://medium.com/data-hackers/confira-o-podcast-de-data-science-e-machine-learning-do-data-hackers-28d4659843b7
Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting

RL agent learnt to sort using significantly fewer array manipulations than Quicksort.

Arxiv: https://arxiv.org/abs/1809.09261
Brilliant post on #CS and #Software about strategy and psychology of Software Development, which is highly applicable to Data Science too.

“Imaginary Problems Are the Root of Bad Software”

https://medium.com/s/story/imaginary-problems-d4f2921bd1b8