I recently gave a presentation and demo for my current application development team about Neural Networks. It was an opportunity to show the team in a very practical, hands-on way the basics of NN and how they might be used. I chose to use real-world data that the team regularly works with, and built a fullstack demo of what it could look like from our development perspective, and from a user perspective.

It's uncommon to see TensorFlow implementations outside of Python, but my team has more experience in TypeScript/JavaScript. I thought it would help them grasp the basic concepts seeing it within their wheelhouse. I got surprisingly good results with a simple ExpressJS and React stack.

I set up two endpoints in the ExpressJS server to demonstrate two different use-cases: a price calculator and plain-language search predictions. The price calculator is a very bare-bones TensorFlow implementation just to introduce some of the high-level concepts to the team. It's a single node that takes a 1-dimensional input, with X values being regular pricing of a shipper, and Y values being historic sale allowances on that shipper. I used the Adam optimizer in TensorFlow to compile the model because of the relatively few training iterations I was doing for the presentation. It uses a simple linear regression loss function to predict what the future sale allowance will be for a given shipper based on the annual ad campaign it is part of.

I used data from two different annual ad campaigns, one with high correlation and one low correlation, to demonstrate how different loss values can affect the resulting model. My hope was that this would show the team a very practical example of some TensorFlow basics.

The second NN I created was to demonstrate the basics of a language model. I will go over that one in a following blog post, including some other helpful materials I used in my TensorFlow presentation.

I set up a repo for the presentation materials I used here:

https://github.com/jdillman1989/tfjs-express-demo