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Hands-On Machine Learning with TensorFlow.js

Hands-On Machine Learning with TensorFlow.js: A guide to building ML applications integrated with web technology using the TensorFlow.js library

By Kai Sasaki

~~NZ$‎57.99~~ NZ$‎14.99

Book Nov 2019 296 pages 1st Edition

~~NZ$‎57.99~~ NZ$‎14.99

~~NZ$‎57.99~~ NZ$‎14.99

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Key benefits

Build, train and run machine learning models in the browser using TensorFlow.js
Create smart web applications from scratch with the help of useful examples
Use flexible and intuitive APIs from TensorFlow.js to understand how machine learning algorithms function

Description

TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use TensorFlow.js to implement various ML models through an example-based approach. Starting with the basics, you'll understand how ML models can be built on the web. Moving on, you will get to grips with the TensorFlow.js ecosystem to develop applications more efficiently. The book will then guide you through implementing ML techniques and algorithms such as regression, clustering, fast Fourier transform (FFT), and dimensionality reduction. You will later cover the Bellman equation to solve Markov decision process (MDP) problems and understand how it is related to reinforcement learning. Finally, you will explore techniques for deploying ML-based web applications and training models with TensorFlow Core. Throughout this ML book, you'll discover useful tips and tricks that will build on your knowledge. By the end of this book, you will be equipped with the skills you need to create your own web-based ML applications and fine-tune models to achieve high performance.

What you will learn

Use the t-SNE algorithm in TensorFlow.js to reduce dimensions in an input dataset Deploy tfjs-converter to convert Keras models and load them into TensorFlow.js Apply the Bellman equation to solve MDP problems Use the k-means algorithm in TensorFlow.js to visualize prediction results Create tf.js packages with Parcel, Webpack, and Rollup to deploy web apps Implement tf.js backend frameworks to tune and accelerate app performance

What do you get with eBook?

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DRM FREE - Read whenever, wherever and however you want

Product Details

Publication date : Nov 27, 2019

Length 296 pages

Edition : 1st Edition

Language : English

ISBN-13 : 9781838821739

Vendor :

Google

Category :

Languages :

Python (Intermediate)

Concepts :

Machine Learning

Tools :

Tensorflow.js (Beginner) , TensorFlow (Beginner)

Table of Contents

17 Chapters

Preface

Who this book is for

What this book covers

To get the most out of this book

1. Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js

Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js

2. Machine Learning for the Web

Machine Learning for the Web

Technical requirements

Why machine learning on the web?

Operation graphs

What is TensorFlow.js?

Installing TensorFlow.js

The low-level API

Further reading

3. Importing Pretrained Models into TensorFlow.js

Importing Pretrained Models into TensorFlow.js

Technical requirements

The portable model format

Exporting a model from TensorFlow

Converting models using tfjs-converter

Loading the model into TensorFlow.js

Further reading

4. TensorFlow.js Ecosystem

TensorFlow.js Ecosystem

Technical requirements

Why high-level libraries?

Using existing models

Loading the data from various kinds of storage

Pose detection with ML5.js

Drawing cats with Magenta.js

XOR classification with machinelearn.js

Further reading

5. Section 2: Real-World Applications of TensorFlow.js

Section 2: Real-World Applications of TensorFlow.js

6. Polynomial Regression

Polynomial Regression

Technical requirements

What is polynomial regression?

Two-dimensional curve fitting

Further reading

7. Classification with Logistic Regression

Classification with Logistic Regression

Technical requirements

Background of binary classification

What is logistic regression?

Classifying two-dimensional clusters

Further reading

8. Unsupervised Learning

Unsupervised Learning

Technical requirements

What is unsupervised learning?

Learning how K-means works

Generalizing K-means with the EM algorithm

Clustering two groups in a 2D space

Further reading

9. Sequential Data Analysis

Sequential Data Analysis

Technical requirements

What is Fourier transformation?

Cosine curve decomposition

Further reading

10. Dimensionality Reduction

Dimensionality Reduction

Technical requirements

Why dimensionality reduction?

Understanding principal component analysis

Projecting 3D points into a 2D space with PCA

Further reading

11. Solving the Markov Decision Process

Solving the Markov Decision Process

Technical requirements

Reinforcement learning

Solving the four-states environment

Further reading

12. Section 3: Productionizing Machine Learning Applications with TensorFlow.js

Section 3: Productionizing Machine Learning Applications with TensorFlow.js

13. Deploying Machine Learning Applications

Deploying Machine Learning Applications

Technical requirements

The ecosystem around the JavaScript platform

Deploying modules with GitHub Pages

Further reading

14. Tuning Applications to Achieve High Performance

Tuning Applications to Achieve High Performance

Technical requirements

The backend API of TensorFlow.js

Tensor management

Asynchronous data access

Model visualization

Further reading

15. Future Work Around TensorFlow.js

Future Work Around TensorFlow.js

Technical requirements

Experimental backend implementations

AutoML edge helper

Further Reading

16. Other Books You May Enjoy

Other Books You May Enjoy

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About the author

Kai Sasaki

Kai Sasaki works as a software engineer at Treasure Data. He engages in developing largescale distributed systems to make data valuable. His passion for creating artificial intelligence by processing large-scale data led him to the field of machine learning. He is one of the initial contributors to TensorFlow.js and keeps working to add new operators that are required for new types of machine learning models. Because of his work, he received the Google Open Source Peer Bonus in 2018.

See other products by Kai Sasaki

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