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Deep Learning with JavaScript (Neural networks in TensorFlow.js)
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Product Details
Author:
Shanqing Cai, Stan Bileschi, Eric Nielsen
Format:
Paperback
Pages:
560
Publisher:
Manning (February 11, 2020)
Language:
English
ISBN-13:
9781617296178
ISBN-10:
1617296171
Weight:
32.72oz
Dimensions:
7.38" x 9.25" x 1.1"
File:
Eloquence-SimonSchuster_05022026_P10038138_onix30_Complete-20260502.xml
Folder:
Eloquence
List Price:
$49.99
Case Pack:
14
As low as:
$44.99
Publisher Identifier:
P-SS
Discount Code:
G
Pub Discount:
37
Imprint:
Manning
Overview
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.
Foreword by Nikhil Thorat and Daniel Smilkov.
About the technology
Running deep learning applications in the browser or on Node-based backends opens up exciting possibilities for smart web applications. With the TensorFlow.js library, you build and train deep learning models with JavaScript. Offering uncompromising production-quality scalability, modularity, and responsiveness, TensorFlow.js really shines for its portability. Its models run anywhere JavaScript runs, pushing ML farther up the application stack.
About the book
In Deep Learning with JavaScript, you’ll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing, image recognition, and self-learning game AI, you’ll master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation.
What's inside
- Image and language processing in the browser
- Tuning ML models with client-side data
- Text and image creation with generative deep learning
- Source code samples to test and modify
About the reader
For JavaScript programmers interested in deep learning.
About the author
Shanging Cai, Stanley Bileschi and Eric D. Nielsen are software engineers with experience on the Google Brain team, and were crucial to the development of the high-level API of TensorFlow.js. This book is based in part on the classic, Deep Learning with Python by François Chollet.
TOC:
PART 1 - MOTIVATION AND BASIC CONCEPTS
1 • Deep learning and JavaScript
PART 2 - A GENTLE INTRODUCTION TO TENSORFLOW.JS
2 • Getting started: Simple linear regression in TensorFlow.js
3 • Adding nonlinearity: Beyond weighted sums
4 • Recognizing images and sounds using convnets
5 • Transfer learning: Reusing pretrained neural networks
PART 3 - ADVANCED DEEP LEARNING WITH TENSORFLOW.JS
6 • Working with data
7 • Visualizing data and models
8 • Underfitting, overfitting, and the universal workflow of machine learning
9 • Deep learning for sequences and text
10 • Generative deep learning
11 • Basics of deep reinforcement learning
PART 4 - SUMMARY AND CLOSING WORDS
12 • Testing, optimizing, and deploying models
13 • Summary, conclusions, and beyond
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.
Foreword by Nikhil Thorat and Daniel Smilkov.
About the technology
Running deep learning applications in the browser or on Node-based backends opens up exciting possibilities for smart web applications. With the TensorFlow.js library, you build and train deep learning models with JavaScript. Offering uncompromising production-quality scalability, modularity, and responsiveness, TensorFlow.js really shines for its portability. Its models run anywhere JavaScript runs, pushing ML farther up the application stack.
About the book
In Deep Learning with JavaScript, you’ll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing, image recognition, and self-learning game AI, you’ll master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation.
What's inside
- Image and language processing in the browser
- Tuning ML models with client-side data
- Text and image creation with generative deep learning
- Source code samples to test and modify
About the reader
For JavaScript programmers interested in deep learning.
About the author
Shanging Cai, Stanley Bileschi and Eric D. Nielsen are software engineers with experience on the Google Brain team, and were crucial to the development of the high-level API of TensorFlow.js. This book is based in part on the classic, Deep Learning with Python by François Chollet.
TOC:
PART 1 - MOTIVATION AND BASIC CONCEPTS
1 • Deep learning and JavaScript
PART 2 - A GENTLE INTRODUCTION TO TENSORFLOW.JS
2 • Getting started: Simple linear regression in TensorFlow.js
3 • Adding nonlinearity: Beyond weighted sums
4 • Recognizing images and sounds using convnets
5 • Transfer learning: Reusing pretrained neural networks
PART 3 - ADVANCED DEEP LEARNING WITH TENSORFLOW.JS
6 • Working with data
7 • Visualizing data and models
8 • Underfitting, overfitting, and the universal workflow of machine learning
9 • Deep learning for sequences and text
10 • Generative deep learning
11 • Basics of deep reinforcement learning
PART 4 - SUMMARY AND CLOSING WORDS
12 • Testing, optimizing, and deploying models
13 • Summary, conclusions, and beyond
