Regression in Angular using TensorFlow.js

  • Post category:StudyBullet-14
  • Reading time:5 mins read


Learn to build regression models to datasets using machine learning in Typescript

What you will learn

Creating regression models in TensorFlowjs

Basics of training a machine learning model

Basics of regression

Building smart apps in Angular

Description

Data Science is all about finding information/knowledge from datasets. One very powerful approach is using linear models, called regression. Even though they are limited, they still can delivery something if the datasets have a linear tendency.

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 a framework to create frontends.


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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 is that they claim it is possible to transform models in both directions: TensorFlow.js <-> TensorFlow.

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!

English
language

Content

Tips on coding

Tips on learning and reusing code code snippet
Interpreting the training graph

Discussing a simple regression model: when we may have to decide the best model

Introduction
How useful are simple regression models?
Guiding your model: Absolute Error Summation vs. Mean Squared Error
How useful are simple regression models?
The mechanics of teaching a neural model: validation vs. training

Nonlinear regression and neural networks

Initial words
Back to the future: imagine you could send a machine learning model backward
Misconception: machine learning does not understand what it is doing
Machine learning concept: features

Building a linear regression to predict perceived temperature

Preparing the environment (with the help of chatGPT)
Fixing initial issues, and we are good to go!
Running a simple model, just to get familiar with TensorFlow.js
Objetive ignorace: mechanical judgment vs. clinical judgment

Uploading and visualizing our dataset

Uploading our dataset from Google Spreadsheet
Taking a look on our dataset
Getting to know our code for plotting

Creating and training our model in TensorFlow.js

Initial information
Getting familiar with our code
Learning to read the training curves
Batch size on the training curve
Creating tensors from our dataset for training
Final settings for finally training our model
Finally our hard work pays off: training the model
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