Deep Learning with JavaScript

Neural networks in TensorFlow.js

by Shanqing Cai, Stanley Bileschi, Eric D. Nielsen, Francois Chollet

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Book Description

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.

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.

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.

Free download in PDF format is not available. You can read Deep Learning with JavaScript book online for free.

Table of Contents

Chapter 1
Deep learning and JavaScript
Chapter 2
Getting started: Simple linear regression in TensorFlow.js
Chapter 3
Adding nonlinearity: Beyond weighted sums
Chapter 4
Recognizing images and sounds using convnets
Chapter 5
Transfer learning: Reusing pretrained neural networks
Chapter 6
Working with data
Chapter 7
Visualizing data and models
Chapter 8
Underfitting, overfitting, and the universal workflow of machine learning
Chapter 9
Deep learning for sequences and text
Chapter 10
Generative deep learning
Chapter 11
Basics of deep reinforcement learning
Chapter 12
Testing, optimizing, and deploying models
Chapter 13
Summary, conclusions, and beyond
Appendix A
Installing tfjs-node-gpu and its dependencies
Appendix B
A quick tutorial of tensors and operations in TensorFlow.js

Book Details

Computer Science
ISBN13 Digital
ISBN10 Digital
All rights reserved

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