visualization   284367

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Designing for Data Visualization – Design at IBM – Medium
Arin Bhowmick – Как визуализируют сложную графику, данные в IBM
Data  ibm  visualization  data+visualisation  tables  infographic 
3 hours ago by akimkin
Claus O. Wilke, Fundamentals of Data Visualization
The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations. Over the years, I have noticed that the same issues arise over and over. I have attempted to collect my accumulated knowledge from these interactions in the form of this book.
ggplot2  Visualization 
8 hours ago by vivalosburros
GAN Lab: Play with Generative Adversarial Networks in Your Browser!
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
machine-learning  ai  ml  generative-adversarial-networks  visualization 
12 hours ago by jonmoore
For Example (Mike Bostock)
Mike's amazing essay about how he shares lots of examples while working on a project
visualization 
17 hours ago by amitp
GitHub - ChrisKnott/Algojammer: An experimental code editor for writing algorithms

Algojammer is an experimental, proof-of-concept code editor for writing algorithms in Python. It was mainly written to assist with solving the kind of algorithm problems that feature in competitions like Google Code Jam, Topcoder and HackerRank.
Inspired by Bret Victor's talks
editor  python  visualization 
18 hours ago by amitp
[1710.00992] DimReader: Axis lines that explain non-linear projections
Non-linear dimensionality reduction (NDR) methods such as LLE and t-SNE are popular with visualization researchers and experienced data analysts, but present serious problems of interpretation. In this paper, we present DimReader, a technique that recovers readable axes from such techniques. DimReader is based on analyzing infinitesimal perturbations of the dataset with respect to variables of interest. The perturbations define exactly how we want to change each point in the original dataset and we measure the effect that these changes have on the projection. The recovered axes are in direct analogy with the axis lines (grid lines) of traditional scatterplots. We also present methods for discovering perturbations on the input data that change the projection the most. The calculation of the perturbations is efficient and easily integrated into programs written in modern programming languages. We present results of DimReader on a variety of NDR methods and datasets both synthetic and real-life, and show how it can be used to compare different NDR methods. Finally, we discuss limitations of our proposal and situations where further research is needed.
user-interface  visualization  dimension-reduction  rather-interesting  data-analysis  explanation  the-mangle-in-practice  to-write-about  to-do 
yesterday by Vaguery

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