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

🎂🎉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

All the statistical distributions and how they relate to each other!

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

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

​​Intuitive 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

​​Exploring 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

​​Model 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

​​HiPlot: 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: pip install hiplot

#hyperopt #facebook #opensource

​​Counting 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

Interesting idea for using GitHub panes for data #visualization

Source: https://twitter.com/levelsio/status/1443133071230791680
Live: https://nomadlist.com/open

🦜 Hi!

We are the first Telegram Data Science channel.

Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.


Ultimate Posts

* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda


Open Data Science

ODS.ai is an international community of people anyhow related to Data Science.

Website: https://ods.ai



Hashtags

Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.

#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
#productionml #sota #recommendation #embeddings #selfdriving #dataset #opensource #analytics #statistics #attention #machine #translation #visualization


Chats

- Data Science Chat https://t.me/datascience_chat
- ODS Slack through invite form at website

ODS resources

* Main website: https://ods.ai
* ODS Community Telegram Channel (in Russian): @ods_ru
* ML trainings Telegram Channel: @mltrainings
* ODS Community Twitter: https://twitter.com/ods_ai

Feedback and Contacts

You are welcome to reach administration through telegram bot: @opendatasciencebot

Some stats to get the perspective of the development of #dalle

«Used 1000 prompts in Dalle over the last 2 days, about 9 hours each day. Of those, saved ~300. 50 I like enough to share w/ socials. 12 enough to rework for future projects. 3 were perfect, may mint someday. Curation is *1* step of AI art. I used to finish physicals in less time.»

Source: https://twitter.com/clairesilver12/status/1550709299797577729

#visualization #gan #generation #generatinveart #aiart #artgentips

Tips & Tricks on Image Generation

Generating images with AI tools is a skill, which can be improved and enhanced. So here is couple of articles, covering tips & tricks on how to generate better images with #midjourney. Most interesting one is #huggingface prompt generator, which uses #NLP model to generate sample prompts.

As an example, we tried to reproduce and improve our group avatar, following ideas in the articles. Prompt for an illustration to this post was generated with query ferrofluids in form of a brain, beautiful connections chaos, swirling black network --ar 3:4 --iw 9 --q 2 --s 1250

Midjourney Prompt Generator: https://huggingface.co/spaces/doevent/prompt-generator
List of Midjourney prompts: https://www.followchain.org/midjourney-prompts/
An advanced guide to writing prompts for Midjourney ( text-to-image): https://medium.com/mlearning-ai/an-advanced-guide-to-writing-prompts-for-midjourney-text-to-image-aa12a1e33b6

#visualization #gan #generation #generatinveart #aiart #artgentips