Thoughtful Machine Learning. A Test-Driven Approach

Spis treści książki

• Preface
  • What to Expect from This Book
  • How to Read This Book
  • Who This Book Is For
  • How to Contact Me
  • Conventions Used in This Book
  • Using Code Examples
  • Safari Books Online
  • How to Contact Us
  • Acknowledgments
• 1. Test-Driven Machine Learning
  • History of Test-Driven Development
  • TDD and the Scientific Method
    • TDD Makes a Logical Proposition of Validity
      • Example: Proof through axioms and functional tests
      • Example: Proof through sufficient conditions, unit tests, and integration tests
    • TDD Involves Writing Your Assumptions Down on Paper or in Code
    • TDD and Scientific Method Work in Feedback Loops
      • Example: Peer review
  • Risks with Machine Learning
    • Unstable Data
    • Underfitting
    • Overfitting
    • Unpredictable Future
  • What to Test for to Reduce Risks
    • Mitigate Unstable Data with Seam Testing
    • Example: Seam testing a neural network
    • Check Fit by Cross-Validating
    • Example: Cross-validating a model
    • Reduce Overfitting Risk by Testing the Speed of Training
    • Example: Benchmark testing
    • Monitor for Future Shifts with Precision and Recall
  • Conclusion
• 2. A Quick Introduction to Machine Learning
  • What Is Machine Learning?
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  • What Can Machine Learning Accomplish?
  • Mathematical Notation Used Throughout the Book
  • Conclusion
• 3. K-Nearest Neighbors Classification
  • History of K-Nearest Neighbors Classification
  • House Happiness Based on a Neighborhood
  • How Do You Pick K?
    • Guessing K
    • Heuristics for Picking K
      • Use coprime class and K combinations
      • Choose a K that is greater or equal to the number of classes + 1
      • Choose a K that is low enough to avoid noise
    • Algorithms for Picking K
  • What Makes a Neighbor Near?
    • Minkowski Distance
    • Mahalanobis Distance
  • Determining Classes
  • Beard and Glasses Detection Using KNN and OpenCV
    • The Class Diagram
    • Raw Image to Avatar
    • The Face Class
      • Testing the Face class
    • The Neighborhood Class
      • Bootstrapping the neighborhood with faces
      • Cross-validation and finding K
  • Conclusion
• 4. Naive Bayesian Classification
  • Using Bayes Theorem to Find Fraudulent Orders
    • Conditional Probabilities
    • Inverse Conditional Probability (aka Bayes Theorem)
  • Naive Bayesian Classifier
    • The Chain Rule
    • Naivety in Bayesian Reasoning
    • Pseudocount
  • Spam Filter
    • The Class Diagram
    • Data Source
    • Email Class
    • Tokenization and Context
    • The SpamTrainer
    • Storing training data
    • Building the Bayesian classifier
    • Calculating a classification
    • Error Minimization Through Cross-Validation
    • Minimizing false positives
    • Building the two folds
    • Cross-validation and error measuring
  • Conclusion
• 5. Hidden Markov Models
  • Tracking User Behavior Using State Machines
    • Emissions/Observations of Underlying States
    • Simplification through the Markov Assumption
    • Using Markov Chains Instead of a Finite State Machine
    • Hidden Markov Model
  • Evaluation: Forward-Backward Algorithm
    • Using User Behavior
  • The Decoding Problem through the Viterbi Algorithm
  • The Learning Problem
  • Part-of-Speech Tagging with the Brown Corpus
    • The Seam of Our Part-of-Speech Tagger: CorpusParser
    • Writing the Part-of-Speech Tagger
    • Cross-Validating to Get Confidence in the Model
    • How to Make This Model Better
  • Conclusion
• 6. Support Vector Machines
  • Solving the Loyalty Mapping Problem
  • Derivation of SVM
  • Nonlinear Data
    • The Kernel Trick
    • Homogenous polynomial
    • Heterogenous polynomial
    • Radial basis functions
    • When should you use each kernel?
    • Soft Margins
    • Optimizing with slack
    • Trading off margin maximization with slack variable minimization using C
  • Using SVM to Determine Sentiment
    • The Class Diagram
    • Corpus Class
    • Tokenization of text
    • Sentiment leaning, :positive or :negative
    • Sentiment codes for :positive and :negative
    • Return a Unique Set of Words from the Corpus
    • The CorpusSet Class
    • Zip two corpus objects
    • Build a sparse vector that ties into SentimentClassifier
    • The SentimentClassifier Class
    • Refactoring the interaction with CorpusSet
    • Library to handle Support Vector Machines: LibSVM
    • Training data
    • Cross-validating with the movie review data
    • Improving Results Over Time
  • Conclusion
• 7. Neural Networks
  • History of Neural Networks
  • What Is an Artificial Neural Network?
    • Input Layer
      • Standard inputs
      • Symmetric inputs
    • Hidden Layers
    • Neurons
      • Activation functions
    • Output Layer
    • Training Algorithms
      • The delta rule
      • Back Propagation
      • QuickProp
      • RProp
  • Building Neural Networks
    • How Many Hidden Layers?
    • How Many Neurons for Each Layer?
    • Tolerance for Error and Max Epochs
  • Using a Neural Network to Classify a Language
    • Writing the Seam Test for Language
    • Cross-Validating Our Way to a Network Class
    • Tuning the Neural Network
    • Convergence Testing
    • Precision and Recall for Neural Networks
    • Wrap-Up of Example
  • Conclusion
• 8. Clustering
  • User Cohorts
  • K-Means Clustering
    • The K-Means Algorithm
    • The Downside of K-Means Clustering
  • Expectation Maximization (EM) Clustering
  • The Impossibility Theorem
  • Categorizing Music
    • Gathering the Data
    • Analyzing the Data with K-Means
    • EM Clustering
    • EM Jazz Clustering Results
  • Conclusion
• 9. Kernel Ridge Regression
  • Collaborative Filtering
  • Linear Regression Applied to Collaborative Filtering
  • Introducing Regularization, or Ridge Regression
  • Kernel Ridge Regression
  • Wrap-Up of Theory
  • Collaborative Filtering with Beer Styles
    • Data Set
    • The Tools We Will Need
    • Reviewer
    • Writing the Code to Figure Out Someones Preference
    • Collaborative Filtering with User Preferences
  • Conclusion
• 10. Improving Models and Data Extraction
  • The Problem with the Curse of Dimensionality
  • Feature Selection
  • Feature Transformation
  • Principal Component Analysis (PCA)
  • Independent Component Analysis (ICA)
  • Monitoring Machine Learning Algorithms
    • Precision and Recall: Spam Filter
    • The Confusion Matrix
  • Mean Squared Error
  • The Wilds of Production Environments
  • Conclusion
• 11. Putting It All Together
  • Machine Learning Algorithms Revisited
  • How to Use This Information for Solving Problems
  • Whats Next for You?
• Index

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