TensorFlow.js : Machine Learning in the Browser and Beyond With JavaScript

The concept of doing machine learning in JavaScript in the browser seems ludicrous at first blush. The reality is, however, it makes all the sense in the world. The question is how to do so performantly.

We will introduce you to a variety of use cases of why this makes sense and how Google has managed to make it a reality through a combination of WebGL, WebAssembly, CUDA, and more.

We will cover:

• Motivations for this crazy idea in the first place
• How to achieve portable performance
• Building applications that reuse existing models
• Caching strategies for large models
• Training models in the browser
• Combining browser capabilities such as images and video with machine learning algorithms

About Brian Sletten

Brian Sletten is a liberal arts-educated software engineer with a focus on forward-leaning technologies. His experience has spanned many industries including retail, banking, online games, defense, finance, hospitality and health care. He has a B.S. in Computer Science from the College of William and Mary and lives in Auburn, CA. He focuses on web architecture, resource-oriented computing, social networking, the Semantic Web, AI/ML, data science, 3D graphics, visualization, scalable systems, security consulting and other technologies of the late 20th and early 21st Centuries. He is also a rabid reader, devoted foodie and has excellent taste in music. If pressed, he might tell you about his International Pop Recording career.

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