Workshop: Tensorflow.js : Machine Learning In and Out of the Browser

Machine Learning : An Overview

 

Machine Learning is all the rage, but many developers have no idea what it is, what they can expect from it or how to start to get into this huge and rapidly-changing field. The ideas draw from the fields of Artificial Intelligence, Numerical Analysis, Statistics and more. These days, you'll generally have to be a CUDA-wielding Python developer to boot. This workshop will gently introduce you to the ideas and tools, show you several working examples and help you build a plan to for diving deeper into this exciting new field.

We will not assume a research background, excessive amounts of statistics knowledge, the ability to calculus your way out of a paper bag, or your being a card-carrying members of the Linear Algebra Fan Club.

 

 

Tensorflow.js : Machine Learning In and Out of the Browser

 

Several trends in our industry are colliding and creating new opportunities while complicating our technology choices. The Web continues to be the major platform for deploying modern applications in lightweight, zero-installation and cross-platform environments. Machine Learning is emerging as a way of managing the explosion of data that we no longer have the capacity to approach with conventional strategies. Hardware is increasingly crucial to making these machine learning systems possible. Layered architectures that embrace mobile, edge and cloud computing complicate where data and code land.

At the confluence of these trends is a need to be able to run machine learning applications in browsers which do not provide direct access to hardware acceleration. There are plans for this to be the case, but until then, Tensorflow.js provides a path forward by leveraging the availability of JavaScript in and out of the browser while getting hardware acceleration where it can. This means the WebGL standard for 3D graphics in the browser and native libraries outside in an environment like Node.js.

This gives us the capacity to move machine learning inference to the edge where many people have powerful GPU capabilities, do not need to push potentially sensitive data to a third party service, and decisions can be localized without the need for the latency of invoking backend services. Beyond these new capabilities, Tensorflow.js represents some very deep thinking about these trends and how to deal with them.

We will cover using pre-trained models that can be fetched remotely as well as the ability to train them directly in the browser. There will be several fun and exciting demonstrations and examples to run and work through.