Jonker-Volgenant Algorithm + t-SNE = Super Powers: https://blog.sourced.tech/post/lapjv/
#tsne #visualization
#tsne #visualization
How to Create Animated Graphs in Python
Short tutorial on how to create animated graphs, which also works in #jupyter notebooks.
Link: https://towardsdatascience.com/how-to-create-animated-graphs-in-python-bb619cc2dec1
Youtube link with #visualization: https://youtu.be/7xrvuSDLHiY|
#novice #beginner
Short tutorial on how to create animated graphs, which also works in #jupyter notebooks.
Link: https://towardsdatascience.com/how-to-create-animated-graphs-in-python-bb619cc2dec1
Youtube link with #visualization: https://youtu.be/7xrvuSDLHiY|
#novice #beginner
Medium
How to Create Animated Graphs in Python
Matplotlib and Seaborn are some nice libraries in Python to create great looking plots. But these plots are all static and itβs hard toβ¦
ππNew 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:
#visualization #dataviz
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
ββNeural 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
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
All the statistical distributions and how they relate to each other!
Source: http://www.math.wm.edu/~leemis/2008amstat.pdf
#distributions #visualization #cheatsheet #statistics
Source: http://www.math.wm.edu/~leemis/2008amstat.pdf
#distributions #visualization #cheatsheet #statistics
Dynamic relationship visualization project
Two kill two birds with one stone, we should also share this #visualization tool.
http://distributome.org/tools.html
#statistics #distributions
Two kill two birds with one stone, we should also share this #visualization tool.
http://distributome.org/tools.html
#statistics #distributions
www.distributome.org
Distributome Tools Carousel
Probability and Statistics Distributions Resource - Distributome
This media is not supported in your browser
VIEW IN TELEGRAM
California wildfire #visualization
How weather conditions during California's fire season have evolved over time.
How weather conditions during California's fire season have evolved over time.
ββDimensionality 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
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
ββIntuitive Visualization of Outlier Detection Methods
Mostly with Python Outlier Detection.
Link: https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html
#outliers #dataviz #visualization
Mostly with Python Outlier Detection.
Link: https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html
#outliers #dataviz #visualization
ββExploring Neural Networks with Activation Atlases
Amazing interactive article on feature visualizations, letting us see through the eyes of the neural network. The hidden layers of neural networks are quite fun to inspect.
Interactive website: https://distill.pub/2019/activation-atlas/
#CV #DL #visualization
Amazing interactive article on feature visualizations, letting us see through the eyes of the neural network. The hidden layers of neural networks are quite fun to inspect.
Interactive website: https://distill.pub/2019/activation-atlas/
#CV #DL #visualization
ββModel for tweaking graph visualization layout parameters
New #MachineLearning model builds a WYSIWYG interface to intuitively produce a layout you want!
Demo: http://kwonoh.net/dgl
Paper: http://arxiv.org/abs/1904.12225
#Visualization #ML
New #MachineLearning model builds a WYSIWYG interface to intuitively produce a layout you want!
Demo: http://kwonoh.net/dgl
Paper: http://arxiv.org/abs/1904.12225
#Visualization #ML
ββHiPlot: High-dimensional interactive plots made easy
Interactive parameters' performance #visualization tool. This new Facebook AI's release enables researchers to more easily evaluate the influence of their hyperparameters, such as learning rate, regularizations, and architecture.
Link: https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy
Github: https://github.com/facebookresearch/hiplot
Demo: https://facebookresearch.github.io/hiplot/_static/demo/demo_basic_usage.html
Pip:
#hyperopt #facebook #opensource
Interactive parameters' performance #visualization tool. This new Facebook AI's release enables researchers to more easily evaluate the influence of their hyperparameters, such as learning rate, regularizations, and architecture.
Link: https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy
Github: https://github.com/facebookresearch/hiplot
Demo: https://facebookresearch.github.io/hiplot/_static/demo/demo_basic_usage.html
Pip:
pip install hiplot
#hyperopt #facebook #opensource
ββCounting Happiness and Where it Comes From
Researches asked 10 000 Mechanical Turk participants to name 10 things which are making them happy, resulting in creation of HappyDB.
And since that DB is open, Nathan Yau analyzed and vizualized this database in the perspective of subjects and actions, producing intersting visualization.
Hope that daily reading @opendatascience makes you at least content, if not happy.
Happines reason visualization link: https://flowingdata.com/2021/07/29/counting-happiness
HappyDB link: https://megagon.ai/projects/happydb-a-happiness-database-of-100000-happy-moments/
#dataset #emotions #visualization
Researches asked 10 000 Mechanical Turk participants to name 10 things which are making them happy, resulting in creation of HappyDB.
And since that DB is open, Nathan Yau analyzed and vizualized this database in the perspective of subjects and actions, producing intersting visualization.
Hope that daily reading @opendatascience makes you at least content, if not happy.
Happines reason visualization link: https://flowingdata.com/2021/07/29/counting-happiness
HappyDB link: https://megagon.ai/projects/happydb-a-happiness-database-of-100000-happy-moments/
#dataset #emotions #visualization
π2
Interesting idea for using GitHub panes for data #visualization
Source: https://twitter.com/levelsio/status/1443133071230791680
Live: https://nomadlist.com/open
Source: https://twitter.com/levelsio/status/1443133071230791680
Live: https://nomadlist.com/open