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.

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