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Practical Machine Learning in JavaScript - by  Charlie Gerard (Paperback) - 1 of 1

Practical Machine Learning in JavaScript - by Charlie Gerard (Paperback)

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About this item

Highlights

  • Build machine learning web applications without having to learn a new language.
  • About the Author: Charlie Gerard is a Senior front-end developer at Netlify, a Google Developer Expert in Web Technologies, and a Mozilla Tech Speaker.
  • 323 Pages
  • Computers + Internet, Intelligence (AI) & Semantics

Description



Book Synopsis



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.




From the Back Cover



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 already honed skills as a web developer, you'll add a whole new field of development to your skill 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. Get started in machine learning with web technologies.

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.

You will:
  • Use the JavaScript framework for ML
  • Build machine learning applications for the web
  • Develop dynamic and intelligent web content



About the Author



Charlie Gerard is a Senior front-end developer at Netlify, a Google Developer Expert in Web Technologies, and a Mozilla Tech Speaker. She is passionate about exploring the possibilities of the web and spends her personal time building interactive prototypes using hardware, creative coding, and machine learning. She has been diving into ML in JavaScript for over a year and built a variety of projects. She's excited to share what she's learned and help more developers get started.
Dimensions (Overall): 9.21 Inches (H) x 6.14 Inches (W) x .71 Inches (D)
Weight: 1.05 Pounds
Suggested Age: 22 Years and Up
Number of Pages: 323
Genre: Computers + Internet
Sub-Genre: Intelligence (AI) & Semantics
Publisher: Apress
Format: Paperback
Author: Charlie Gerard
Language: English
Street Date: November 17, 2020
TCIN: 83021215
UPC: 9781484264171
Item Number (DPCI): 247-28-1810
Origin: Made in the USA or Imported
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Shipping details

Estimated ship dimensions: 0.71 inches length x 6.14 inches width x 9.21 inches height
Estimated ship weight: 1.05 pounds
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