Regression in Angular using TensorFlow.js

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Jorge Guerra Pires

2:43:21

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1. Tips on coding

1. Tips on learning and reusing code code snippet.mp4

09:17

2. Interpreting the training graph.mp4

14:56

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

1. Introduction.mp4

05:16

2. How useful are simple regression models.mp4

05:00

3. Guiding your model Absolute Error Summation vs. Mean Squared Error.mp4

04:44

4. How useful are simple regression models.mp4

02:05

5. The mechanics of teaching a neural model validation vs. training.mp4

05:20

3. Nonlinear regression and neural networks

1. Initial words.mp4

04:25

2. Back to the future imagine you could send a machine learning model backward.mp4

01:43

3. Misconception machine learning does not understand what it is doing.mp4

04:35

4. Machine learning concept features.mp4

05:54

4. Building a linear regression to predict perceived temperature

1. Preparing the environment (with the help of chatGPT).mp4

05:11

2. Fixing initial issues, and we are good to go!.mp4

07:52

3. Running a simple model, just to get familiar with TensorFlow.js.mp4

10:16

4. Objetive ignorace mechanical judgment vs. clinical judgment.mp4

01:06

5. Uploading and visualizing our dataset

1. Uploading our dataset from Google Spreadsheet.mp4

11:13

2. Taking a look on our dataset.mp4

05:54

3. Getting to know our code for plotting.mp4

11:06

6. Creating and training our model in TensorFlow.js

1. Initial information.mp4

04:25

2. Getting familiar with our code.mp4

11:49

3. Learning to read the training curves.mp4

11:49

4. Batch size on the training curve.mp4

02:12

5. Add features to the model.html

6. Improving the sampling algorithm.html

7. Creating tensors from our dataset for training.mp4

03:34

8. Final settings for finally training our model.mp4

04:03

9. Finally our hard work pays off training the model.mp4

09:36

10. Create a test graph.html

Description

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

What You'll Learn?

Creating regression models in TensorFlowjs
Basics of training a machine learning model
Basics of regression
Building smart apps in Angular

Who is this for?

JavaScript programmers waiting to learng machine learning

Machine learning practitioners wanting to learn web coding

Angular coders wanting to add intelligence to their apps

What You Need to Know?

I have tried to explain everything, but an Angular knowledge may be advantageous, and a machine learning know also may be useful.

More details

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.
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!

Who this course is for:
JavaScript programmers waiting to learng machine learning
Machine learning practitioners wanting to learn web coding
Angular coders wanting to add intelligence to their apps

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I have been working with computer programming and mathematical modeling applied to biological systems since my bachelor of Engineering.Currently, I am an Independent Researcher and Member of the Center of Excellence for Research DEWS (University of L'Aquila, DISIM, Italy)Short-bio (EN): B.Eng by Universidade Federal de Ouro Preto (Brazil) in Production Engineering; double master degree by University of L'Aquila and Gdansk University of Technology: PhD on a collaboration between the Brazilian programme Science without Borders in biomathematics at the University of L'Aquila/IASI-CNR/BioMathLab. Master of science and PhD degrees recognized in Brazil by University of São Paulo (USP) as bioinformatics. Postdoc by Federal University of Bahia and Fiocruz.Academia Edu140 Followers | 12 Following | 5 Co-authors | 8,654Total Views | top 2%.Biografia curta: Tenho trabalhado com programação de computadores e modelagem computacional aplicado a sistemas biológicos.Alguns detalhas da minha formação:•Sou formado pela UFOP em engenharia de produção;•Tenho mestrado pela Universidade de L’Aquila e Técnica de Gdansk;•Tenho doutorado pela Universidade de L’Aquila;•Tenho um postdoc pela UFBA e terminei um outro pela Fiocruz;•Meus diplomas no exterior foram reconhecidos pela USP em bioinformática;

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