Practical TensorFlow.js: Deep Learning in Web App Development - Softcover

Rivera, Juan De Dios Santos

 
9781484262726: Practical TensorFlow.js: Deep Learning in Web App Development
Softcover
ISBN 10: 1484262727 ISBN 13: 9781484262726
Verlag: Apress, 2020

Inhaltsangabe

Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow.​js​ is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard​, ​ml5js​, ​tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow.​js​ to create intelligent web apps.


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
Who This Book Is For

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.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

Juan De Dios Santos Rivera is a machine learning engineer who focuses on building data-driven and machine learning-driven platforms. As a Big Data Software Engineer for mobile apps, his role has been to build solutions to detect spammers and avoid the proliferation of them. This book goes hand-to-hand with that role in building data solutions. As the AI field keeps growing, developers need to keep extending the reach of our products to every platform out there, which includes web browsers.

Von der hinteren Coverseite

Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow.​js​ is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard​, ​ml5js​, ​tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow.​js​ to create intelligent web apps.


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.

You will:

  • 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

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Apress
Erscheinungsdatum
2020
Sprache
Englisch
ISBN 10 
1484262727
ISBN 13 
9781484262726
Einband
Taschenbuch
Auflage
1
Anzahl der Seiten
328
Kontakt zum Hersteller
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