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Hands-on Machine Learning with JavaScript

You're reading from   Hands-on Machine Learning with JavaScript Solve complex computational web problems using machine learning

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788998246
Length 356 pages
Edition 1st Edition
Languages
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Author (1):
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Burak Kanber Burak Kanber
Author Profile Icon Burak Kanber
Burak Kanber
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Table of Contents (14) Chapters Close

Preface 1. Exploring the Potential of JavaScript FREE CHAPTER 2. Data Exploration 3. Tour of Machine Learning Algorithms 4. Grouping with Clustering Algorithms 5. Classification Algorithms 6. Association Rule Algorithms 7. Forecasting with Regression Algorithms 8. Artificial Neural Network Algorithms 9. Deep Neural Networks 10. Natural Language Processing in Practice 11. Using Machine Learning in Real-Time Applications 12. Choosing the Best Algorithm for Your Application 13. Other Books You May Enjoy

Example - XOR in TensorFlow.js

In this example, we're going to solve the XOR problem using a TensorFlow.js feedforward neural network. First, let's explore the XOR problem, and why it's a good starting point for us.

The XOR, or exclusive or operation, is a Boolean operator that returns true if only one, but not both, of its inputs is truth. Compare this to the regular Boolean OR that you're more commonly familiar with, which will return true if both inputs are true—the XOR will return false if both inputs are true. Here is a table comparing XOR to OR; I've highlighted the case where OR and XOR differ:

Input 1 Input 2 OR XOR
False False False False
False True True True
True False True True
True True True False

Why is the XOR problem a good test for us? Let's plot the XOR operations on a graph:

Viewing the preceding graph, we can...

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Tech Concepts
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Programming languages
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