Machine learning in the browser with Tensorflow.js pt.1

In my previous blog post I discussed perceptrons, a very early example of machine learning. As a recap, perceptrons are simple learning algorithms that can solve linearly separable problems.

Two lines demonstrate the correct and predicted classification of each point on a grid
Perceptron solving a linearly separable problem (source: nature of code)

This is cool, ┬ábut not very useful. As far as I can tell most of the problems a perceptron can solve can be done much more quickly by passing your data through a well considered IF statement (I.e. If coffee mug is in photo then it is a photo of coffee). These days we can see all sorts of applications of machine learning that seem to solve much more complicated problems. Self driving cars are learning what a person looks like, can make assumptions about how they’ll move and can direct a car to respond based on this information. Much of this more advanced machine learning is through multilayer perceptrons, neural nets and other advanced methods.

Single layer perceptron (nature of code)
Multi-layer perceptron
Example of complex Non-linearly seperable data

One of the best way to get started working with these advance machine learning algorithms is through Google’s tensorflow library. This has been available as a python library for some time and was recently updated to include a Javascript library as well. In this post I’m going to cover how to quickly get this running and some basic concepts that you need to understand as you get started. Much of this material is covered in the getting started section on the tensorflow.js website as well.

Read More