Book Image

Building Data-Driven Applications with Danfo.js

By : Rising Odegua, Stephen Oni
Book Image

Building Data-Driven Applications with Danfo.js

By: Rising Odegua, Stephen Oni

Overview of this book

Most data analysts use Python and pandas for data processing for the convenience and performance these libraries provide. However, JavaScript developers have always wanted to use machine learning in the browser as well. This book focuses on how Danfo.js brings data processing, analysis, and ML tools to JavaScript developers and how to make the most of this library to build data-driven applications. Starting with an overview of modern JavaScript, you’ll cover data analysis and transformation with Danfo.js and Dnotebook. The book then shows you how to load different datasets, combine and analyze them by performing operations such as handling missing values and string manipulations. You’ll also get to grips with data plotting, visualization, aggregation, and group operations by combining Danfo.js with Plotly. As you advance, you’ll create a no-code data analysis and handling system and create-react-app, react-table, react-chart, Draggable.js, and tailwindcss, and understand how to use TensorFlow.js and Danfo.js to build a recommendation system. Finally, you’ll build a Twitter analytics dashboard powered by Danfo.js, Next.js, node-nlp, and Twit.js. By the end of this app development book, you’ll be able to build and embed data analytics, visualization, and ML capabilities into any JavaScript app in server-side Node.js or the browser.
Table of Contents (18 chapters)
1
Section 1: The Basics
3
Section 2: Data Analysis and Manipulation with Danfo.js and Dnotebook
10
Section 3: Building Data-Driven Applications

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...