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Hands-On Unsupervised Learning with Python
Unsupervisedlearningisaboutmakinguseofraw,untaggeddataandapplyinglearningalgorithmstoittohelpamachinepredictitsoutcome.Withthisbook,youwillexploretheconceptofunsupervisedlearningtoclusterlargesetsofdataandanalyzethemrepeatedlyuntilthedesiredoutcomeisfoundusingPython.Thisbookstartswiththekeydifferencesbetweensupervised,unsupervised,andsemi-supervisedlearning.Youwillbeintroducedtothebest-usedlibrariesandframeworksfromthePythonecosystemandaddressunsupervisedlearninginboththemachinelearninganddeeplearningdomains.Youwillexplorevariousalgorithms,techniquesthatareusedtoimplementunsupervisedlearninginreal-worldusecases.Youwilllearnavarietyofunsupervisedlearningapproaches,includingrandomizedoptimization,clustering,featureselectionandtransformation,andinformationtheory.Youwillgethands-onexperiencewithhowneuralnetworkscanbeemployedinunsupervisedscenarios.YouwillalsoexplorethestepsinvolvedinbuildingandtrainingaGANinordertoprocessimages.Bytheendofthisbook,youwillhavelearnedtheartofunsupervisedlearningfordifferentreal-worldchallenges.
目錄(185章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- Hands-On Unsupervised Learning with Python
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the author
- About the reviewer
- Packt is searching for authors like you
- 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
- 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
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時(shí)間：2021-07-02 12:32:34
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