Hands-On Unsupervised Learning with Python by Giuseppe Bonaccorso -- Read -- Imperial Library of Trantor

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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