Deep learning in the browser: Explorable explanations, model inference, and rapid prototyping
Amit Kapoor and Bargava Subramanian lead three live demos of deep learning (DL) done in the browserbuilding explorable explanations to aid insight, building model inference applications, and rapid prototyping and training an ML modelusing the emerging client-side JavaScript libraries for DL.
| Talk Title | Deep learning in the browser: Explorable explanations, model inference, and rapid prototyping |
| Speakers | Amit Kapoor (narrativeVIZ), Bargava Subramanian (Binaize) |
| Conference | Strata Data Conference |
| Conf Tag | Making Data Work |
| Location | London, United Kingdom |
| Date | May 22-24, 2018 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
The browser is the most common end-point consumption of deep learning models. It is also the most ubiquitous platform for programming available. The maturity of the client-side JavaScript ecosystem across the deep learning process—Data Frame support (Arrow), WebGL-accelerated learning frameworks (deeplearn.js), declarative interactive visualization (Vega-Lite), etc.—have made it easy to start playing with deep learning in the browser. Amit Kapoor and Bargava Subramanian lead three live demos of deep learning (DL) for explanations, inference, and training done in the browser, using the emerging client-side JavaScript libraries for DL with three different types of data: tabular, text, and image. They also explain how the ecosystem of tools for DL in the browser might emerge and evolve. Demonstrations include: The demos leverage the following libraries in JavaScript: The working demos will be available on the web and as open source code on GitHub.
