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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

What is TensorFlow.js?

TensorFlow.js (tfjs) is a JavaScript library for creating, training, and deploying ML models in the browser or in Node.js. It was created at Google by Nikhil Thorat and Daniel Smilkov and was initially called Deeplearn.js, before being merged into the TensorFlow team in 2018 and renamed as TensorFlow.js.

TensorFlow.js provides two main layers, outlined as follows:

  • CoreAPI: This is the low-level API that deals directly with tensors—the core data structure of TensorFlow.js.
  • LayerAPI: A high-level layer built on top of the CoreAPI layer for easily building ML models.

In later sections, Tensors and basic operations on tensors and Building a simple regression model with TensorFlow.js, you will learn more details about the CoreAPI and LayerAPI layers.

With TensorFlow.js, you can do the following:

  • Perform hardware-accelerated mathematical operations
  • Develop ML models for the browser or Node.js
  • Retrain existing ML models...
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