Table of Contents for
Hands-On Unsupervised Learning Using Python

Version ebook / Retour

Cover image for bash Cookbook, 2nd Edition Hands-On Unsupervised Learning Using Python by Ankur A. Patel Published by O'Reilly Media, Inc., 2019
  1. Cover
  2. nav
  3. Hands-on Unsupervised Learning Using Python
  4. Hands-On Unsupervised Learning Using Python
  5. Preface
  6. I. Fundamentals of Unsupervised Learning
  7. 1. Unsupervised Learning in the Machine Learning Ecosystem
  8. 2. End-to-End Machine Learning Project
  9. II. Unsupervised Learning Using Scikit-Learn
  10. 3. Dimensionality Reduction
  11. 4. Anomaly Detection
  12. 5. Clustering
  13. 6. Group Segmentation
  14. III. Unsupervised Learning using TensorFlow and Keras
  15. 7. Autoencoders
  16. 8. Hands-On Autoencoder
  17. 9. Semi-Supervised Learning
  18. IV. Deep Unsupervised Learning using TensorFlow and Keras
  19. 10. Recommender Systems Using Restricted Boltzmann Machines
  20. 11. Feature Detection Using Deep Belief Networks
  21. 12. Generative Adversarial Networks
  22. 13. Time Series Clustering
  23. 14. Conclusion
  24. Index
  25. About the Author
  1. Preface
    1. A Brief History of Machine Learning
    2. AI is Back, but Why Now?
    3. The Emergence of Applied AI
    4. Major Milestones in Applied AI over the Past 20 Years
    5. From Narrow AI to General AI
    6. Objective and Approach
    7. Prerequisites
    8. Roadmap
    9. Other Resources
    10. Conventions Used in This Book
    11. Using Code Examples
    12. O’Reilly Safari
    13. How to Contact Us
  2. I. Fundamentals of Unsupervised Learning
  3. 1. Unsupervised Learning in the Machine Learning Ecosystem
    1. Basic Machine Learning Terminology
    2. Rules-Based Versus Machine Learning
    3. Supervised Versus Unsupervised
      1. The Strengths and Weaknesses of Supervised Learning
      2. The Strengths and Weaknesses of Unsupervised Learning
    4. Using Unsupervised Learning to Improve Machine Learning Solutions
    5. A Closer Look at Supervised Algorithms
      1. Linear Methods
      2. Neighborhood-Based Methods
      3. Tree-Based Methods
      4. Support Vector Machines
      5. Neural Networks
    6. A Closer Look at Unsupervised Algorithms
      1. Dimensionality Reduction
      2. Clustering
      3. Feature Extraction
      4. Unsupervised Deep Learning
      5. Sequential Data Problems Using Unsupervised Learning
    7. Reinforcement Learning Using Unsupervised Learning
    8. Semi-Supervised Learning
    9. Successful Applications of Unsupervised Learning
    10. Conclusion
  4. 2. End-to-End Machine Learning Project
    1. Environment Setup
      1. Version Control: Git
      2. Clone the Hands-On Unsupervised Learning Git Repository
      3. Scientific Libraries: Anaconda Distribution of Python
      4. Neural Networks: TensorFlow and Keras
      5. Gradient Boosting, Version One: XGBoost
      6. Gradient Boosting, Version Two: LightGBM
      7. Clustering Algorithms
      8. Interactive Computing Environment: Jupyter Notebook
    2. Overview of the Data
    3. Data Preparation
      1. Data Acquisition
      2. Data Exploration
      3. Generate Feature Matrix and Labels Array
      4. Feature Engineering and Feature Selection
      5. Data Visualization
    4. Model Preparation
      1. Split into Training and Test Sets
      2. Select Cost Function
      3. Create k-Fold Cross Validation Sets
    5. Machine Learning Models - Part One
      1. Model One - Logistic Regression
    6. Evaluation metrics
      1. Confusion matrix
      2. Precision-Recall Curve
      3. Receiver Operating Characteristic
    7. Machine Learning Models - Part Two
      1. Model Two - Random Forests
      2. Model Three - Gradient Boosting Machine (XGBoost)
      3. Model Four - Gradient Boosting Machine (LightGBM)
    8. Evaluation of the Four Models Using the Test Set
    9. Ensembles
      1. Stacking
    10. Final Model Selection
    11. Production Pipeline
    12. Conclusion
  5. II. Unsupervised Learning Using Scikit-Learn
  6. 3. Dimensionality Reduction
    1. The Motivation for Dimensionality Reduction
      1. The MNIST Digits Database
    2. Dimensionality Reduction Algorithms
      1. Linear Projection Versus Manifold Learning
    3. Principal Component Analysis (PCA)
      1. PCA, the concept
      2. PCA in Practice
      3. Incremental PCA
      4. Sparse PCA
      5. Kernel PCA
    4. Singular Value Decomposition
    5. Random Projection
      1. Gaussian Random Projection
      2. Sparse Random Projection
    6. Isomap
    7. Multidimensional Scaling (MDS)
    8. Locally Linear Embedding
    9. t-Distributed Stochastic Neighbor Embedding (t-SNE)
    10. Other Dimensionality Reduction Methods
    11. Dictionary Learning
    12. Independent Component Analysis
    13. Conclusion
  7. 4. Anomaly Detection
    1. Credit Card Fraud Detection
      1. Prepare the Data
      2. Define Anomaly Score Function
      3. Define Evaluation Metrics
      4. Define Plotting Function
    2. Normal PCA Anomaly Detection
      1. PCA Components Equal Number of Original Dimensions
      2. Search for the Optimal Number of Principal Components
    3. Sparse PCA Anomaly Detection
    4. Kernel PCA Anomaly Detection
    5. Gaussian Random Projection Anomaly Detection
    6. Sparse Random Projection Anomaly Detection
    7. Non-Linear Anomaly Detection
    8. Dictionary Learning Anomaly Detection
    9. Independent Component Analysis Anomaly Detection
    10. Fraud Detection on the Test Set
      1. Normal PCA Anomaly Detection on the Test Set
      2. Independent Component Analysis Anomaly Detection on the Test Set
      3. Dictionary Learning Anomaly Detection on the Test Set
    11. Conclusion
  8. 5. Clustering
    1. MNIST Digits Dataset
      1. Data Preparation
    2. Clustering Algorithms
    3. K-Means
      1. K-Means Inertia
      2. Evaluating the Clustering Results
      3. K-Means Accuracy
      4. K-Means and the Number of Principal Components
      5. K-Means on the Original Dataset
    4. Hierarchical Clustering
      1. Agglomerative Hierarchical Clustering
      2. The Dendrogram
      3. Evaluating the Clustering Results
    5. DBSCAN
      1. DBSCAN Algorithm
      2. Applying DBSCAN to Our Dataset
      3. HDBSCAN
    6. Conclusion
  9. 6. Group Segmentation
    1. Lending Club Data
      1. Data Preparation
      2. Transform String to Numerical
      3. Impute Missing Values
      4. Engineer Features
      5. Select Final Set of Features and Perform Scaling
      6. Designate Labels for Evaluation
    2. Goodness of the Clusters
    3. K-Means Application
    4. Hierarchical Clustering Application
    5. HDBSCAN Application
    6. Conclusion
  10. III. Unsupervised Learning using TensorFlow and Keras
  11. 7. Autoencoders
    1. Neural Networks
      1. TensorFlow
      2. Keras
    2. Autoencoder - the Encoder and the Decoder
    3. Undercomplete Autoencoders
    4. Overcomplete Autoencoders
    5. Dense Versus Sparse Autoencoders
    6. Denoising Autoencoder (DAE)
    7. Variational Autoencoder (VAE)
    8. Conclusion
  12. 8. Hands-On Autoencoder
    1. Data Preparation
    2. The Components of an Autoencoder
    3. Activation Functions
    4. Our First Autoencoder
      1. Loss Function
      2. Optimizer
      3. Training the Model
      4. Evaluating on the Test Set
    5. Two-Layer Undercomplete Autoencoder with Linear Activation Function
      1. Increasing the Number of Nodes
      2. Adding More Hidden Layers
    6. Non-Linear Autoencoder
    7. Overcomplete Autoencoder with Linear Activation
    8. Overcomplete Autoencoder with Linear Activation and Dropout
    9. Sparse Overcomplete Autoencoder with Linear Activation
    10. Sparse Overcomplete Autoencoder with Linear Activation and Dropout
    11. Working with Noisy Datasets
    12. Denoising Autoencoder
      1. Two-Layer Denoising Undercomplete Autoencoder with Linear Activation
      2. Two-Layer Denoising Overcomplete Autoencoder with Linear Activation
      3. Two-Layer Denoising Overcomplete Autoencoder with ReLu Activation
    13. Conclusion
  13. 9. Semi-Supervised Learning
    1. Data Preparation
    2. Supervised Model
    3. Unsupervised Model
    4. Semi-Supervised Model
    5. The Power of Supervised and Unsupervised
    6. Conclusion
  14. IV. Deep Unsupervised Learning using TensorFlow and Keras
  15. 10. Recommender Systems Using Restricted Boltzmann Machines
    1. Boltzmann Machines
      1. Restricted Boltzmann Machines
    2. Recommender Systems
      1. Collaborative Filtering
      2. The Netflix Prize
    3. MovieLens Dataset
      1. Data Preparation
      2. Define the Cost Function: Mean Squared Error
      3. Perform Baseline Experiments
    4. Matrix Factorization
      1. One Latent Factor
      2. Three Latent Factors
      3. Five Latent Factors
    5. Collaborative Filtering Using RBMs
      1. RBM Neural Network Architecture
      2. Build the Components of the RBM Class
      3. Train RBM Recommender System
    6. Conclusion
  16. 11. Feature Detection Using Deep Belief Networks
    1. Deep Belief Networks in Detail
    2. MNIST Image Classification
    3. Restricted Boltzmann Machines
      1. Build the Components of the RBM Class
      2. Generate Images Using the RBM Model
      3. View the Intermediate Feature Detectors
    4. Train the Three RBMs for the DBN
      1. Examine Feature Detectors
      2. View Generated Images
    5. The Full DBN
      1. How Training of a DBN Works
      2. Train the DBN
    6. How Unsupervised Learning Helps Supervised Learning
      1. Generate Images to Build a Better Image Classifier
    7. Image Classifier Using LightGBM
      1. Supervised Only
      2. Unsupervised and Supervised Solution
    8. Conclusion
  17. 12. Generative Adversarial Networks
    1. GANs, the Concept
      1. The Power of GANs
    2. Deep Convolutional GANs (DCGANs)
    3. Convolutional Neural Networks (CNNs)
    4. DCGANs Revisited
      1. Generator of the DCGAN
      2. Discriminator of the DCGAN
      3. Discriminator and Adversarial Models
      4. DCGAN for the MNIST Dataset
    5. MNIST DCGAN in Action
      1. Synthetic Image Generation
    6. Conclusion
  18. 13. Time Series Clustering
    1. ECG Data
    2. Approach to Time Series Clustering
      1. k-Shape
    3. Time Series Clustering using k-Shape on ECG Five Days
      1. Data Preparation
      2. Training and Evaluation
    4. Time Series Clustering Using k-Shape on ECG 5000
      1. Data Preparation
      2. Training and Evaluation
    5. Time Series Clustering Using k-Means on ECG 5000
    6. Time Series Clustering Using Hierarchical DBSCAN on ECG 5000
    7. Comparing the Time Series Clustering Algorithms
      1. Full Run with k-Shape
      2. Full Run with k-Means
      3. Full Run with HDBSCAN
      4. Compare all three time series clustering approaches
    8. Conclusion
  19. 14. Conclusion
    1. Supervised Learning
    2. Unsupervised Learning
      1. Scikit-Learn
      2. TensorFlow and Keras
    3. Reinforcement Learning
    4. Most Promising Areas of Unsupervised Learning Today
    5. The Future of Unsupervised Learning
    6. Final Words
  20. Index
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