Allocated time to media per person

#statistics #visualization

Source:Nielsen
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@machinelearning_tuts

Tinker With a Neural Network Right Here in Your Browser

This was created by Daniel Smilkov and Shan Carter. This is a continuation of many people’s previous work — most notably Andrej Karpathy’s #convnet.js demo and Chris Olah’s articles about neural networks.

Via: @cedeeplearning

https://playground.tensorflow.org/
#visualization #neural_networks

🔻COVID-19 Visualized: The power of effective visualizations for pandemic storytelling

Clear, succinct data visualizations can be powerful tools for telling stories and explaining phenomena. This article demonstrates this concept as relates to the COVID-19 pandemic.
💡By Matthew Mayo, KDnuggets.

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link: https://www.kdnuggets.com/2020/03/covid-19-visualized.html

📌Via: @cedeeplearning

#visualization
#covid19
#neuralnetworks
#deeplearning

🔻Some points to visualizing your data

📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://policyviz.com/2018/08/07/dataviz-cheatsheet/

#visualization
#bigdata
#machinelearning
#datascience

🔹Top 10 Data Visualization Tools for Every Data Scientist

At present, the data scientist is one of the most sought after professions. That’s one of the main reasons why we decided to cover the latest data visualization tools that every data scientist can use to make their work more effective.

1. Tableau
2. D3
3. Qlikview
4. Microsoft Power BI
5. Datawrapper
6. E Charts
7. Plotly
8. Sisense
9. FusionCharts
10. HighCharts
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.kdnuggets.com/2020/05/top-10-data-visualization-tools-every-data-scientist.html

#datascience #visualization #datatools
#machinelearning #tableau #powerbi

⚪️ Visualizing the world beyond the frame

🔹Researchers test how far artificial intelligence models can go in dreaming up varied poses and colors of objects and animals in photos.

🔹To give computer vision models a fuller, more imaginative view of the world, researchers have tried feeding them more varied images. Some have tried shooting objects from odd angles, and in unusual positions, to better convey their real-world complexity. Others have asked the models to generate pictures of their own, using a form of artificial intelligence called GANs, or generative adversarial networks. In both cases, the aim is to fill in the gaps of image datasets to better reflect the three-dimensional world and make face- and object-recognition models less biased.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: http://news.mit.edu/2020/visualizing-the-world-beyond-the-frame-0506

#deeplearning #GANs #math
#machinelearning #visualization
#AI #MIT #datascience