Charlie Gerard

Practical Machine Learning in JavaScript (eBook, PDF)

TensorFlow.js for Web Developers

• Format: PDF

Produktbeschreibung

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

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Produktdetails

• Verlag: Apress
• Seitenzahl: 323
• Erscheinungstermin: 16. November 2020
• Englisch
• ISBN-13: 9781484264188
• Artikelnr.: 62299127

Autorenporträt

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.

Inhaltsangabe

Chapter 1: The Basics of Machine Learning.- Chapter 2: Tensorflow.js.- Chapter 3: Building an Image Classifier.- Chapter 4:.- Text Classification and Sentiment Analysis.- Chapter 5: Experimenting with Inputs.- Chapter 6: Machine Learning in Production.- Chapter 7: Bias in Machine Learning.