Practical Machine Learning in JavaScript

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.jsan 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.

  Move from basic web development into the field of machine learning Incorporate the ethics of AI into your development considerations Harness your existing skills with JavaScript to learn a new approach to development

  Autorentext

  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.

  Inhalt
  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.