Regression in Angular using TensorFlow.js
Learning to build nonlinear/linear regression models for datasets using machine learning in Typescript (applied math)
Ratings 2.29 / 5.00
What You Will Learn!
- Creating linear/nonlinear regression models in TensorFlowjs
- Basics of training a machine learning model
- Building smart apps in Angular
- Basics of linear/nonlinear regression
- How to use Angular for scientific computation based on machine learning
- Coding in TypeScript by example, basics, hands-on section
Description
Data Science is all about finding information/knowledge from datasets. One very powerful approach is using linear/nonlinear models, called regression; we are not going to see logistic regression on this course, see my other course “Machine learning in Angular”. Linear models, the example we are going to work on, even though they are limited, they still can delivery something if the datasets have a linear tendency. In TensorFlow.js, shifting to nonlinear models is as easy as changing a single parameter; that is all.
TensorFlow.js makes it possible to build simple models to complex ones (e.g., deep learning) using the same notation, it is magic! It is possible thanks to how TensorFlow.js was build, and the power of tensor, where TensorFlow.js is build on.
On this course, we use Angular as framework, coding environment, and TensorFlow.js as the library for creating a machine learning based regression model.
What is Angular??
Angular is a framework, designed by the Google Team, and it has been widely used to design sites. Essentially, it is a framework to create frontends, based on TypeScript. In layman's terms: the page you see and interact on your web browser. It is perfect to be used alongside TensorFlow.js for offering also the advantage of not sending information to the server: you can build everything with Angular, they even have their own server call Angular Universal. As they like to say, Angular comes with batteries.
What is TensoFlow.js??
TensorFlow.js is a JavaScript-based library for deep learning, based on the classical TensorFlow, written in Python; you can also do simple learning machine, some simple mathematical operations with tensors and so on. There are several reasons for using TensorFlow.js instead of Python, and I hope to come back to that in the future.
A nice point, it is possible to transform models in both directions: TensorFlow.js <-> TensorFlow. Even their notations are alike.
We are going to build a linear regression model using TensorFlow.js in Angular. We are also going to learn about machine learning, and Angular!
External resource
- GitBook
- GitHub
- Articles
Update 18/08/23. in response to student feedback, I am upgrading the course, adding more resources.
Who Should Attend!
- JavaScript programmers wanting to learn machine learning
- Machine learning practitioners wanting to learn web coding
- Angular coders wanting to add intelligence to their apps
- Programmers to start on Angular using practical problems