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Building Data-Driven Applications with Danfo.js

You're reading from   Building Data-Driven Applications with Danfo.js A practical guide to data analysis and machine learning using JavaScript

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801070850
Length 476 pages
Edition 1st Edition
Languages
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Authors (2):
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Stephen Oni Stephen Oni
Author Profile Icon Stephen Oni
Stephen Oni
Rising Odegua Rising Odegua
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Rising Odegua
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Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics
2. Chapter 1: An Overview of Modern JavaScript FREE CHAPTER 3. Section 2: Data Analysis and Manipulation with Danfo.js and Dnotebook
4. Chapter 2: Dnotebook - An Interactive Computing Environment for JavaScript 5. Chapter 3: Getting Started with Danfo.js 6. Chapter 4: Data Analysis, Wrangling, and Transformation 7. Chapter 5: Data Visualization with Plotly.js 8. Chapter 6: Data Visualization with Danfo.js 9. Chapter 7: Data Aggregation and Group Operations 10. Section 3: Building Data-Driven Applications
11. Chapter 8: Creating a No-Code Data Analysis/Handling System 12. Chapter 9: Basics of Machine Learning 13. Chapter 10: Introduction to TensorFlow.js 14. Chapter 11: Building a Recommendation System with Danfo.js and TensorFlow.js 15. Chapter 12: Building a Twitter Analysis Dashboard 16. Chapter 13: Appendix: Essential JavaScript Concepts 17. Other Books You May Enjoy

Why you need Danfo.js

To successfully bring a machine learning project written in Python to the web, there are a lot of processes and tasks to be carried out, things such as model deployment, creating API routes with frameworks such as Flask, FastAPI, or Django, and then using JavaScript to send HTTP requests to the model. You can clearly observe that the process involves a lot of JavaScript. It would be super awesome if we could perform all these processes in just JavaScript, wouldn't it? Well, the good news is that we can.

Over the past years, browsers have steadily increased in computational power and can support highly intensive computation, hence giving JavaScript the edge to challenge Python when it comes to data-intensive tasks.

With the help of Node.js, JavaScript has access to the GPU available on local computers, giving us the ability to undergo a full-stack machine learning project using Node.js for the backend and pure JavaScript for the frontend.

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