D3 allows you to bind arbitrary data to a Document Object Model (DOM), and then apply data-driven transformations to the document. For example, you can use D3 to generate an HTML table from an array of numbers. Or, use the same data to create an interactive SVG bar chart with smooth transitions and interaction.
D3 is not a monolithic framework that seeks to provide every conceivable feature. Instead, D3 solves the crux of the problem: efficient manipulation of documents based on data. This avoids proprietary representation and affords extraordinary flexibility, exposing the full capabilities of web standards such as HTML, SVG, and CSS. With minimal overhead, D3 is extremely fast, supporting large datasets and dynamic behaviors for interaction and animation. D3’s functional style allows code reuse through a diverse collection of official and community-developed modules.
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Based on our record, D3.js seems to be a lot more popular than Apple Machine Learning Journal. While we know about 167 links to D3.js, we've tracked only 7 mentions of Apple Machine Learning Journal. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Do you mean something for data visualization, or tricks condensing large data sets with cursors? https://d3js.org/ Best of luck =3. - Source: Hacker News / 2 months ago
Document address: D3.js Official Document. - Source: dev.to / 5 months ago
D3.js: One of the most popular JavaScript visualization libraries. - Source: dev.to / 7 months ago
A Dependency is an npm package that our code depends on in order to be able to run. Some popular packages that can be added as dependencies are lodash, D3, and chartjs. - Source: dev.to / 7 months ago
RacingBars is an open-source, light-weight (~45kb gzipped), easy-to-use, and feature-rich javascript library for bar chart race, based on D3.js. - Source: dev.to / 9 months ago
Https://machinelearning.apple.com Fun fact: Their first paper, Improving the Realism of Synthetic Images (2017; https://machinelearning.apple.com/research/gan), strongly hints at eye and hand tracking for the Apple Vision Pro released 5 years later. - Source: Hacker News / 9 months ago
For your reference, Apple's pages for Machine Learning for Developers and for their research. The Apple Neural Engine was custom designed to work better with their proprietary machine learning programs -- and they've been opening up access to developers by extending support / compatibility for TensorFlow and PyTorch. They've also got CoreML, CreateML, and various APIs they are making to allow more use of their... Source: almost 2 years ago
We even host annual poster sessions of those PhD intern’s work while at our company, and it’ll give you an idea of the caliber of work. It may not be as great as Nvidia, Stryker, Waymo, or Tesla (which are not part of MAANG but I believe are far more ahead in CV), but it’s worth of considering. Source: about 2 years ago
They have something for ML: https://machinelearning.apple.com. - Source: Hacker News / about 3 years ago
They're more subtle about it, I think. https://machinelearning.apple.com/ Some of the papers are pretty good. I don't disagree with your sentiment in aggregate, though. Source: about 3 years ago
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