Hands-on Machine Learning with JavaScript电子书

Title Page

Copyright and Credits

Hands-On Machine Learning with JavaScript

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Contributors

About the author

About the reviewer

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Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Exploring the Potential of JavaScript

Why JavaScript?

Why machine learning, why now?

Advantages and challenges of JavaScript

The CommonJS initiative

Node.js

TypeScript language

Improvements in ES6

Let and const

Classes

Module imports

Arrow functions

Object literals

The for...of function

Promises

The async/await functions

Preparing the development environment

Installing Node.js

Optionally installing Yarn

Creating and initializing an example project

Creating a Hello World project

Summary

Data Exploration

An overview

Feature identification

The curse of dimensionality

Feature selection and feature extraction

Pearson correlation example

Cleaning and preparing data

Handling missing data

Missing categorical data

Missing numerical data

Handling noise

Handling outliers

Transforming and normalizing data

Summary

Tour of Machine Learning Algorithms

Introduction to machine learning

Types of learning

Unsupervised learning

Supervised learning

Measuring accuracy

Supervised learning algorithms

Reinforcement learning

Categories of algorithms

Clustering

Classification

Regression

Dimensionality reduction

Optimization

Natural language processing

Image processing

Summary

Grouping with Clustering Algorithms

Average and distance

Writing the k-means algorithm

Setting up the environment

Initializing the algorithm

Testing random centroid generation

Assigning points to centroids

Updating centroid locations

The main loop

Example 1 – k-means on simple 2D data

Example 2 – 3D data

k-means where k is unknown

Summary

Classification Algorithms

k-Nearest Neighbor

Building the KNN algorithm

Example 1 – Height, weight, and gender

Example 2 – Decolorizing a photo

Naive Bayes classifier

Tokenization

Building the algorithm

Example 3 – Movie review sentiment

Support Vector Machine

Random forest

Summary

Association Rule Algorithms

The mathematical perspective

The algorithmic perspective

Association rule applications

Example – retail data

Summary

Forecasting with Regression Algorithms

Regression versus classification

Regression basics

Example 1 – linear regression

Example 2 – exponential regression

Example 3 – polynomial regression

Other time-series analysis techniques

Filtering

Seasonality analysis

Fourier analysis

Summary

Artificial Neural Network Algorithms

Conceptual overview of neural networks

Backpropagation training

Example - XOR in TensorFlow.js

Summary

Deep Neural Networks

Convolutional Neural Networks

Convolutions and convolution layers

Example – MNIST handwritten digits

Recurrent neural networks

SimpleRNN

Gated recurrent units

Long Short-Term Memory

Summary

Natural Language Processing in Practice

String distance

Term frequency - inverse document frequency

Tokenizing

Stemming

Phonetics

Part of speech tagging

Word embedding and neural networks

Summary

Using Machine Learning in Real-Time Applications

Serializing models

Training models on the server

Web workers

Continually improving and per-user models

Data pipelines

Data querying

Data joining and aggregation

Transformation and normalization

Storing and delivering data

Summary

Choosing the Best Algorithm for Your Application

Mode of learning

The task at hand

Format, form, input, and output

Available resources

When it goes wrong

Combining models

Summary

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