[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
[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
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
[unable to retrieve full-text content]Since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity.
Original Post: Top 15 Python Libraries for Data Science in 2017
Posted by Shan Carter, Software Engineer and Chris Olah, Research Scientist, Google Brain TeamScience isn’t just about discovering new results. It’s also about human understanding. Scientists need to develop notations, analogies, visualizations, and explanations of ideas. This human dimension of science isn’t a minor side project. It’s deeply tied to the heart of science.That’s why, in collaboration with OpenAI, DeepMind, YC Research, and others, we’re excited to announce the launch of Distill, a new open science journal and ecosystem supporting human understanding of machine learning. Distill is an independent organization, dedicated to fostering a new segment of the research community.Modern web technology gives us powerful new tools for expressing this human dimension of science. We can create interactive diagrams and user interfaces the enable intuitive exploration of research ideas. Over the last few years we’ve seen many incredible demonstrations of…
Original Post: Distill: Supporting Clarity in Machine Learning
Welcome to the R graph gallery, a collection of R graph examples, organized by chart type, searchable by R function, with reproducible code and explanation. By Yan Holtz, INRA. Data visualization is one of the key steps of the Data Science Process. There are hundreds of possibilities when it comes to visualizing data – choosing and using the right one is…
Original Post: The R Graph Gallery Data Visualization Collection
This is a collection of tutorials relating to the results of the recent KDnuggets algorithms poll. If you are interested in learning or brushing up on the most used algorithms, as per our readers, look here for suggestions on doing so! KDnuggets recently ran a poll asking our readers “Which methods/algorithms you used in the past 12 months for an…
Original Post: The Great Algorithm Tutorial Roundup