Creating Simple Data Visualizations as an Act of Kindness

[unable to retrieve full-text content]The field of data visualization is still quite young and evolving rapidly—and tools like the web and VR are continuing to expand the possibilities. So there is a lot of room for exploring new possibilities and creating new formats, as well as many examples of novel and amazing visualizations.
Original Post: Creating Simple Data Visualizations as an Act of Kindness

Interactive visualizations of sampling and GP regression

Interactive visualizations of sampling and GP regression You really don’t want to miss Chi Feng‘s absolutely wonderful interactive demos. (1) Markov chain Monte Carlo sampling I believe this is exactly what Andrew was asking for a few Stan meetings ago: This tool lets you explore a range of sampling algorithms including random-walk Metropolis, Hamiltonian Monte Carlo, and NUTS operating over a range of two-dimensional distributions (standard normal, banana, donut, multimodal, and one squiggly one). You can control both the settings of the algorithms and the settings of the visualizations. As you run it, it even collects the draws into a sample which it summarizes as marginal histograms. Source code The demo is implemented in Javascript with the source code on Chi Feng’s GitHub organization: Wish list 3D (glasses or virtual reality headset) multiple chains in parallel scatterplot breadcrumbs Gibbs sampler…
Original Post: Interactive visualizations of sampling and GP regression

Visualizing Convolutional Neural Networks with Open-source Picasso

[unable to retrieve full-text content]Toolkits for standard neural network visualizations exist, along with tools for monitoring the training process, but are often tied to the deep learning framework. Could a general, easy-to-setup tool for generating standard visualizations provide a sanity check on the learning process?
Original Post: Visualizing Convolutional Neural Networks with Open-source Picasso

Beautiful Python Visualizations: An Interview with Bryan Van de Ven, Bokeh Core Developer

[unable to retrieve full-text content]Read this insightful interview with Bokeh’s core developer, Bryan Van de Ven, and gain an understanding of what Bokeh is, when and why you should use it, and what makes Bryan a great fit for helming this project.
Original Post: Beautiful Python Visualizations: An Interview with Bryan Van de Ven, Bokeh Core Developer

Facets: An Open Source Visualization Tool for Machine Learning Training Data

Posted by James Wexler, Senior Software Engineer, Google Big Picture Team(Cross-posted on the Google Open Source Blog)Getting the best results out of a machine learning (ML) model requires that you truly understand your data. However, ML datasets can contain hundreds of millions of data points, each consisting of hundreds (or even thousands) of features, making it nearly impossible to understand an entire dataset in an intuitive fashion. Visualization can help unlock nuances and insights in large datasets. A picture may be worth a thousand words, but an interactive visualization can be worth even more.Working with the PAIR initiative, we’ve released Facets, an open source visualization tool to aid in understanding and analyzing ML datasets. Facets consists of two visualizations that allow users to see a holistic picture of their data at different granularities. Get a sense of the shape of…
Original Post: Facets: An Open Source Visualization Tool for Machine Learning Training Data