7th Workshop on Visualization for AI Explainability October 13, 2024 at IEEE VIS in St. Pete Beach, Florida The role of visualization in artificial intelligence (AI) gained significant attention in recent years. With the growing complexity of AI models, the critical need for understanding their inner-workings has increased. Visualization is potentially a powerful technique to fill such a critical
MLU-EXPLAIN Visual explanations of core machine learning concepts Machine Learning University (MLU) is an education initiative from Amazon designed to teach machine learning theory and practical application. As part of that goal, MLU-Explain exists to teach important machine learning concepts through visual essays in a fun, informative, and accessible manner. Neural Networks Learn about neural net
Dash apps give a point-&-click interface to models written in Python, vastly expanding the notion of what's possible in a traditional "dashboard." With Dash apps, data scientists and engineers put complex Python analytics in the hands of business decision-makers and operators. When building Dash apps in a business setting, you'll need Dash Enterprise to deploy and scale them, plus integrate them w
Visdom has a simple set of features that can be composed for various use-cases. Windows The UI begins as a blank slate – you can populate it with plots, images, and text. These appear in windows that you can drag, drop, resize, and destroy. The windows live in envs and the state of envs is stored across sessions. You can download the content of windows – including your plots in svg. Tip: You can u
KEY Black values are sorted. Gray values are unsorted. A red triangle marks the algorithm position. Dark gray values denote the current interval (shell, merge, quick). A pair of red triangles marks the left and right pointers (quick). DISCUSSIONThese pages show 8 different sorting algorithms on 4 different initial conditions. These visualizations are intended to: Show how each algorithm operates.
: In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions. Keep scrolling. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco. First, some intuition Let’s say you had to determine whether a home is
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out. Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R. Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects ar
Cubism.js is a D3 plugin for visualizing time series. Use Cubism to construct better realtime dashboards, pulling data from Graphite, Cube and other sources. Cubism is available under the Apache License on GitHub. Scalable Cubism fetches time series data incrementally: after the initial display, Cubism reduces server load by polling only the most recent values. Cubism renders incrementally, too, u
My favorite macOS app This is by far one of the most useful tools I have used and one of the reasons I even stick with Macs instead of going to Windows. WiFi explorer has been used for countless projects and it comes in handy for troubleshooting and optimizing. I have used it recently to help install two Google WiFi setups at different houses with different layouts. Being able to see all of the de
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Currently, we have visualizations for the following data structures and algorithms: Basics Stack: Array Implementation Stack: Linked List Implementation Queues: Array Implementation Queues: Linked List Implementation Lists: Array Implementation (available in java version) Lists: Linked List Implementation (available in java version) Recursion Factorial Reversing a String N-Queens Problem Indexing
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