Fr. 48.80

Deep Learning with JavaScript

English · Paperback / Softback

Shipping usually within 3 to 5 weeks

Description

Read more

Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R.

Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.

  • Deploying computer vision, audio, and natural language processing in the browser
  • Fine-tuning machine learning models with client-side data
  • Constructing and training a neural network
  • Interactive AI for browser games using deep reinforcement learning
  • Generative neural networks to generate music and pictures
TensorFlow.js is an open-source JavaScript library for defining, training, and deploying deep learning models to the web browser. It's quickly gaining popularity with developers for its amazing set of benefits including scalability, responsiveness, modularity, and portability.

Shanging Cai and Eric Nielsen are senior software engineers on the Google Brain team.

Stan Bileschi is the technical lead for Google's TensorFlow Usability team, which built the TensorFlow Layers API. All three have advanced degrees from MIT. Together, they're responsible for writing most of TensorFlow.js.

About the author

Shanging Cai and Eric Nielsen are senior software engineers on the Google Brain team.
 
Stan Bileschi is the technical lead for Google’s TensorFlow Usability team, which built the TensorFlow Layers API. All three have advanced degrees from MIT. Together, they’re responsible for writing most of TensorFlow.js.

Summary

Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R.
 
Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.

  • Deploying computer vision, audio, and natural language processing in the browser
  • Fine-tuning machine learning models with client-side data
  • Constructing and training a neural network
  • Interactive AI for browser games using deep reinforcement learning
  • Generative neural networks to generate music and pictures
TensorFlow.js is an open-source JavaScript library for defining, training, and deploying deep learning models to the web browser. It’s quickly  gaining  popularity with developers for its amazing set of benefits including scalability, responsiveness, modularity, and portability.
 
Shanging Cai and Eric Nielsen are senior software engineers on the Google Brain team.
 
Stan Bileschi is the technical lead for Google’s TensorFlow Usability team, which built the TensorFlow Layers API. All three have advanced degrees from MIT. Together, they’re responsible for writing most of TensorFlow.js.

Product details

Authors Stan Bileschi, Stanley Bileschi, Shanqing Cai, Fran?ois Chollet, Francois Chollet, François Chollet, Eric Nielsen, Eric Nielsen, Eric D Nielsen, Shanqing Cai, Stan Bileschi
Publisher Manning Publications
 
Languages English
Product format Paperback / Softback
Released 28.03.2020
 
EAN 9781617296178
ISBN 978-1-61729-617-8
Dimensions 185 mm x 237 mm x 31 mm
Weight 1021 g
Subject Natural sciences, medicine, IT, technology > IT, data processing > Programming languages

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.