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

#tsne #visualization
πŸŽ‚πŸŽ‰New Release - #Matplotlib 3.0.0. Supports Python 3. Highlights include:

GUI backend is selected at run-time based on what toolkits are installed;
New cyclic color map *twilight*;
Improvements to automatic layout of titles, ticks & GridSpec.

mail thread: https://mail.python.org/pipermail/matplotlib-announce/2018-September/000027.html
official site: https://matplotlib.org/users/whats_new.html
installation: pip install -U matplotlib

#visualization #dataviz
​​Neural network 3D visualization framework. Very nice in-depth visualizations.

Now you can actually see how the layers look.

Github: https://github.com/tensorspace-team/tensorspace
LiveDemo (!): https://tensorspace.org/html/playground/vgg16.html

#visualization #nn
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California wildfire #visualization

How weather conditions during California's fire season have evolved over time.
​​Dimensionality reduction for visualizing single-cell data using UMAP

UMAP is an t-SNE replacement for #visualization.

UMAP is being increasingly accepted as a powerful tool for visualizing single cell datasets. This paper compares UMAP to #TSNE

While UMAP is unquestionably better than default t-SNE in preserving global structure, it's worth mentioning that (very recently) it was shown that this limitation of t-SNE appears to be addressable with better parameters/initialization.

Article link: https://www.nature.com/articles/nbt.4314