Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Deep learning in the browser: Explorable explanations, model inference, and rapid prototyping

Amit Kapoor (narrativeVIZ Consulting), Bargava Subramanian (Independent)
16:3517:15 Thursday, 24 May 2018
Data science and machine learning, Visualization and user experience
Location: Capital Suite 13 Level: Intermediate

Who is this presentation for?

  • Data scientists, BI and analytics application developers, data engineers, and anyone interested in learning and doing ML and DL

Prerequisite knowledge

  • A basic understanding of machine learning and deep learning concepts

What you'll learn

  • Explore three live demos of deep learning (DL) done in the browser

Description

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 e.g. dataframe support (arrow.js), WebGl accelerated learning frameworks (deeplearn.js), declarative interactive visualisation (vega-lite.js), 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:

  1. Explorable explanations: Explaining the DL model and allowing the users to build intuition on the model helps generate insight. The explorable explanation for a loan default DL model allows the user to explore the feature space and threshold boundaries using interactive visualizations to drive decision making.
  2. Model inference: Inference is the most common use case. The browser allows you to bring your DL model to the data and also allows you test how the model works when executed on the edge. The demonstrated comments sentiment application can identify and warn users about the toxicity of your comments as you type in a text box.
  3. Rapid prototyping: Training DL models is now possible in the browser itself, if done smartly. The rapid prototyping image classification example allows the user to play with transfer learning to build a model specific for a user-generated image input.

The demos leverage the following libraries in JavaScript:

  • Arrow for data loading, and type inference
  • Facets for exploratory data analysis
  • ml.js for traditional machine learning model training and inference
  • Deeplearn.js for deep learning model training and inference
  • Vega and Vega-Lite for interactive dashboards

The working demos will be available on the web and as open source code on GitHub.

Photo of Amit Kapoor

Amit Kapoor

narrativeVIZ Consulting

Amit Kapoor is interested in learning and teaching the craft of telling visual stories with data. At narrativeVIZ Consulting, Amit uses storytelling and data visualization as tools for improving communication, persuasion, and leadership through workshops and trainings conducted for corporations, nonprofits, colleges, and individuals. Amit also teaches storytelling with data as guest faculty in executive courses at IIM Bangalore and IIM Ahmedabad. Amit’s background is in strategy consulting, using data-driven stories to drive change across organizations and businesses. He has more than 12 years of management consulting experience with AT Kearney in India, Booz & Company in Europe, and more recently for startups in Bangalore. Amit holds a BTech in mechanical engineering from IIT, Delhi, and a PGDM (MBA) from IIM, Ahmedabad. Find more about him at Amitkaps.com.

Photo of Bargava Subramanian

Bargava Subramanian

Independent

Bargava Subramanian is a machine learning engineer based in Bangalore, India. Bargava has 14 years’ experience delivering business analytics solutions to investment banks, entertainment studios, and high-tech companies. He has given talks and conducted numerous workshops on data science, machine learning, deep learning, and optimization in Python and R around the world. He mentors early-stage startups in their data science journey. Bargava holds a master’s degree in statistics from the University of Maryland at College Park. He is an ardent NBA fan.

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