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: @haarrp
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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
Fast image-to-image translation in the browser. With 3 trained models introduced. Plus a processed dataset of 1000 images for edges2cats translation.

Demo: https://zaidalyafeai.github.io/pix2pix/cats.html
Code: https://github.com/zaidalyafeai/zaidalyafeai.github.io/tree/master/pix2pix

#tf #tensorflow #tfjs #pix2pix #cv
​​Separate voice from music

Spleeter is the Deezer source separation library with pretrained models written in Python and uses Tensorflow. It makes it easy to train source separation model (assuming you have a dataset of isolated sources), and provides already trained state of the art model for performing various flavor of separation:
* vocals (singing voice) / accompaniment separation (2 stems)
* vocals / drums / bass / other separation (4 stems)
* vocals / drums / bass / piano / other separation (5 stems)

Spleeter is also very fast as it can perform separation of audio files to 4 stems 100x faster than real-time when run on a GPU

blog: https://deezer.io/releasing-spleeter-deezer-r-d-source-separation-engine-2b88985e797e
paper: http://archives.ismir.net/ismir2019/latebreaking/000036.pdf
github: https://github.com/deezer/spleeter

#voice #music #tf