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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

Tensors and basic operations on tensors

A tensor is a basic data structure in tfjs. You can think of tensors as a generalization of vectors, matrices, or high-dimensional arrays. The CoreAPI, which we introduced in the What is TensorFlow.js? section, exposes different functions for creating and working with tensors.

The following screenshot shows a simple comparison between scalars, vectors, and a matrix with a tensor:

Figure 10.5 – Comparison between simple n-dimensional arrays and a tensor

Tip

A matrix is a grid of m x n numbers, where m represents the number of rows and n represents the number of columns. A matrix can be of one or more dimensions, and matrixes of the same shape support direct mathematical operations on each other.

A vector, on the other hand, is a one-dimensional matrix with shape (1, 1); that is, it has a single row and column—for example, [2, 3], [3, 1, 4].

We mentioned earlier that a tensor is more of a generalized...

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