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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 (18) Chapters Close

Title Page
Packt Upsell
Contributors
Preface
1. Exploring the Potential of JavaScript 2. Data Exploration FREE CHAPTER 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 1. Other Books You May Enjoy Index

Chapter 8. Artificial Neural Network Algorithms

Artificial Neural Networks (ANNs) or, simply NNs, are arguably the most popular machine learning (ML) tool today, if not necessarily the most widely used. The tech media and commentary of the day love to focus on neural networks, and they are seen by many as the magical algorithm. It is believed that neural networks will pave the way to Artificial General Intelligence (AGI)—but the technical reality is much different.

While they are powerful, neural networks are highly specialized ML models that focus on solving individual tasks or problems—they are not magical brains that can solve problems out of the box. A model that exhibits 90% accuracy is typically considered good. Neural networks are slow to train and require thoughtful design and implementation. That said, they are indeed highly proficient problem solvers that can unravel even very difficult problems, such as object identification in images.

It is likely that neural networks will play...

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