Overview
Focus less on deep learning concepts, and the functionalities of the framework, and more on actual applications using JavaScript
Work with a wide range of algorithms, methods, and use cases, such as convolutional neural networks, object detection, image translation, and linear regression
Build a real and deployed deep learning product using JavaScript and TensorFlow.js
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Table of contents (11 chapters)
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Front Matter
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Back Matter
Keywords
About this book
The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN). Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis.
Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js.
What You'll Learn
- Build deep learning products suitable for web browsers
- Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN)
- Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis
Programmers developing deep learning solutions for the web and those who want to learn TensorFlow.js with at least minimal programming and software development knowledge. No prior JavaScript knowledge is required, but familiarity with it is helpful.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Practical TensorFlow.js
Book Subtitle: Deep Learning in Web App Development
Authors: Juan De Dios Santos Rivera
DOI: https://doi.org/10.1007/978-1-4842-6273-3
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Professional and Applied Computing (R0), Apress Access Books
Copyright Information: Juan De Dios Santos Rivera 2020
Softcover ISBN: 978-1-4842-6272-6Published: 19 September 2020
eBook ISBN: 978-1-4842-6273-3Published: 18 September 2020
Edition Number: 1
Number of Pages: XXIV, 303
Number of Illustrations: 67 b/w illustrations
Topics: Artificial Intelligence