Hands-On Unsupervised Learning with Python电子书

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Hands-On Unsupervised Learning with Python

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Preface

Who this book is for

What this book covers

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Download the example code files

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Conventions used

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Getting Started with Unsupervised Learning

Technical requirements

Why do we need machine learning?

Descriptive analysis

Diagnostic analysis

Predictive analysis

Prescriptive analysis

Types of machine learning algorithm

Supervised learning algorithms

Supervised hello world!

Unsupervised learning algorithms

Cluster analysis

Generative models

Association rules

Unsupervised hello world!

Semi-supervised learning algorithms

Reinforcement learning algorithms

Why Python for data science and machine learning?

Summary

Questions

Further reading

Clustering Fundamentals

Technical requirements

Introduction to clustering

Distance functions

K-means

K-means++

Analysis of the Breast Cancer Wisconsin dataset

Evaluation metrics

Minimizing the inertia

Silhouette score

Completeness score

Homogeneity score

A trade-off between homogeneity and completeness using the V-measure

Adjusted Mutual Information (AMI) score

Adjusted Rand score

Contingency matrix

K-Nearest Neighbors

Vector Quantization

Summary

Questions

Further reading

Advanced Clustering

Technical requirements

Spectral clustering

Mean shift

DBSCAN

Calinski-Harabasz score

Analysis of the Absenteeism at Work dataset using DBSCAN

Cluster instability as a performance metric

K-medoids

Online clustering

Mini-batch K-means

BIRCH

Comparison between mini-batch K-means and BIRCH

Summary

Questions

Further reading

Hierarchical Clustering in Action

Technical requirements

Cluster hierarchies

Agglomerative clustering

Single and complete linkages

Average linkage

Ward's linkage

Analyzing a dendrogram

Cophenetic correlation as a performance metric

Agglomerative clustering on the Water Treatment Plant dataset

Connectivity constraints

Summary

Questions

Further reading

Soft Clustering and Gaussian Mixture Models

Technical requirements

Soft clustering

Fuzzy c-means

Gaussian mixture

EM algorithm for Gaussian mixtures

Assessing the performance of a Gaussian mixture with AIC and BIC

Component selection using Bayesian Gaussian mixture

Generative Gaussian mixture

Summary

Questions

Further reading

Anomaly Detection

Technical requirements

Probability density functions

Anomalies as outliers or novelties

Structure of the dataset

Histograms

Kernel density estimation (KDE)

Gaussian kernel

Epanechnikov kernel

Exponential kernel

Uniform (or Tophat) kernel

Estimating the density

Anomaly detection

Anomaly detection with the KDD Cup 99 dataset

One-class support vector machines

Anomaly detection with Isolation Forests

Summary

Questions

Further reading

Dimensionality Reduction and Component Analysis

Technical requirements

Principal Component Analysis (PCA)

PCA with Singular Value Decomposition

Whitening

PCA with the MNIST dataset

Kernel PCA

Adding more robustness to heteroscedastic noise with factor analysis

Sparse PCA and dictionary learning

Non-Negative Matrix Factorization

Independent Component Analysis

Topic modeling with Latent Dirichlet Allocation

Summary

Questions

Further reading

Unsupervised Neural Network Models

Technical requirements

Autoencoders

Example of a deep convolutional autoencoder

Denoising autoencoders

Adding noise to the deep convolutional autoencoder

Sparse autoencoders

Adding a sparseness constraint to the deep convolutional autoencoder

Variational autoencoders

Example of a deep convolutional variational autoencoder

Hebbian-based principal component analysis

Sanger's network

An example of Sanger's network

Rubner-Tavan's network

An example of a Rubner-Tavan's network

Unsupervised deep belief networks

Restricted Boltzmann Machines

Deep belief networks

Example of an unsupervised DBN

Summary

Questions

Further reading

Generative Adversarial Networks and SOMs

Technical requirements

Generative adversarial networks

Analyzing a GAN

Mode collapse

Example of a deep convolutional GAN

Wasserstein GANs

Transforming the DCGAN into a WGAN

Self-organizing maps

Example of a Kohonen map

Summary

Questions

Further reading

Assessments

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

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