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Practical Machine Learning in JavaScript: TensorFlow.js for Web Developers [9781484264171]

Paperback
SKU: 9781484264171
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Format Lightweight and affordable. Perfect for student groups and classrooms, and a versatile option for corporate trainings, team reads, or large-scale events.

Price $54.99

Total for 25 copies:

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Overview

Build machine learning web applications without having to learn a new language. This book will help you develop basic knowledge of machine learning concepts and applications. 

You’ll learn not only theory, but also dive into code samples and example projects with TensorFlow.js. Using these skills and your knowledge as a web developer, you’ll add a whole new field of development to your tool set. This will give you a more concrete understanding of the possibilities offered by machine learning. Discover how ML will impact the future of not just programming in general, but web development specifically. 

Machine learning is currently one of the most exciting technology fields with the potential to impact industries from health to home automation to retail, and even art. Google has now introduced TensorFlow.js—an iteration of TensorFlow aimed directly at web developers. Practical Machine Learning in JavaScript will help you stay relevant in the tech industry with new tools, trends, and best practices.

What You'll Learn
  • Use the JavaScript framework for ML
  • Build machine learning applications for the web
  • Develop dynamic and intelligent web content
Who This Book Is For

Web developers and who want a hands-on introduction to machine learning in JavaScript. A working knowledge of the JavaScript language is recommended. 




The book, Practical Machine Learning in JavaScript: TensorFlow.js for Web Developers [Bulk, Wholesale, Quantity] ISBN#9781484264171 in Paperback by Charlie Gerard may be ordered in bulk quantities. Minimum starts at 25 copies. Availability based on publisher status and quantity being ordered.

Details

Author:
Charlie Gerard
Format:
Paperback
Publication Date:
11/17/2020
ISBN-13:
9781484264171
Pages:
323
Publisher:
Apress

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