Scala for Machine Learning - Second Edition

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• Scala for Machine Learning Second Edition
  • Table of Contents
  • Scala for Machine Learning Second Edition
  • Credits
  • About the Author
  • About the Reviewers
  • www.PacktPub.com
    • eBooks, discount offers, and more
      • Why subscribe?
  • Customer Feedback
  • Preface
    • What this book covers
    • What you need for this book
    • Who this book is for
    • Conventions
    • Reader feedback
    • Customer support
      • Downloading the example code
      • Downloading the color images of this book
      • Errata
      • Piracy
      • Questions
  • 1. Getting Started
    • Mathematical notations for the curious
    • Why machine learning?
      • Classification
      • Prediction
      • Optimization
      • Regression
    • Why Scala?
      • Scala as a functional language
        • Abstraction
        • Higher kinded types
        • Functors
        • Monads
      • Scala as an object oriented language
      • Scala as a scalable language
    • Model categorization
    • Taxonomy of machine learning algorithms
      • Unsupervised learning
        • Clustering
        • Dimension reduction
      • Supervised learning
        • Generative models
      • Discriminative models
      • Semi-supervised learning
      • Reinforcement learning
    • Leveraging Java libraries
    • Tools and frameworks
      • Java
      • Scala
        • Eclipse Scala IDE
        • IntelliJ IDEA Scala plugin
      • Simple build tool
      • Apache Commons Math
        • Description
        • Licensing
        • Installation
      • JFreeChart
        • Description
        • Licensing
        • Installation
      • Other libraries and frameworks
    • Source code
      • Convention
        • Context bounds
        • Presentation
        • Primitives and implicits
        • Immutability
    • Lets kick the tires
      • Writing a simple workflow
        • Step 1 scoping the problem
        • Step 2 loading data
        • Step 3 preprocessing data
          • Immutable normalization
        • Step 4 discovering patterns
          • Analyzing data
          • Plotting data
            • Visualizing model features
            • Visualizing label
        • Step 5 implementing the classifier
          • Selecting an optimizer
          • Training the model
          • Classifying observations
        • Step 6 evaluating the model
    • Summary
  • 2. Data Pipelines
    • Modeling
      • What is a model?
      • Model versus design
      • Selecting features
      • Extracting features
    • Defining a methodology
    • Monadic data transformation
      • Error handling
      • Monads to the rescue
        • Implicit models
        • Explicit models
    • Workflow computational model
      • Supporting mathematical abstractions
        • Step 1 variable declaration
        • Step 2 model definition
        • Step 3 instantiation
      • Composing mixins to build workflow
        • Understanding the problem
        • Defining modules
        • Instantiating the workflow
      • Modularizing
    • Profiling data
      • Immutable statistics
      • Z-score and Gauss
    • Assessing a model
      • Validation
        • Key quality metrics
        • F-score for binomial classification
        • F-score for multinomial classification
      • Area under the curves
        • Area under PRC
        • Area under ROC
      • Cross-validation
        • One-fold cross-validation
        • K-fold cross-validation
      • Bias-variance decomposition
      • Overfitting
    • Summary
  • 3. Data Preprocessing
    • Time series in Scala
      • Context bounds
      • Types and operations
        • Transpose operator
        • Differential operator
      • Lazy views
    • Moving averages
      • Simple moving average
      • Weighted moving average
      • Exponential moving average
    • Fourier analysis
      • Discrete Fourier transform (DFT)
      • DFT-based filtering
      • Detection of market cycles
    • The discrete Kalman filter
      • The state space estimation
        • The transition equation
        • The measurement equation
      • The recursive algorithm
        • Prediction
        • Correction
        • Kalman smoothing
        • Fixed lag smoothing
        • Experimentation
        • Benefits and drawbacks
    • Alternative preprocessing techniques
    • Summary
  • 4. Unsupervised Learning
    • K-mean clustering
      • K-means
        • Measuring similarity
        • Defining the algorithm
        • Step 1 Clusters configuration
          • Defining clusters
          • Initializing clusters
        • Step 2 Clusters assignment
        • Step 3 Reconstruction error minimization
          • Creating K-means components
          • Tail recursive implementation
          • Iterative implementation
        • Step 4 Classification
        • Curse of dimensionality
        • Evaluation
        • The results
        • Tuning the number of clusters
        • Validation
    • Expectation-Maximization (EM)
      • Gaussian mixture model
      • EM overview
      • Implementation
      • Classification
      • Testing
      • Online EM
    • Summary
  • 5. Dimension Reduction
    • Challenging model complexity
    • The divergences
      • The Kullback-Leibler divergence
        • Overview
        • Implementation
        • Testing
      • The mutual information
    • Principal components analysis (PCA)
      • Algorithm
      • Implementation
      • Test case
      • Evaluation
      • Extending PCA
        • Validation
        • Categorical features
        • Performance
    • Nonlinear models
      • Kernel PCA
      • Manifolds
    • Summary
  • 6. Nave Bayes Classifiers
    • Probabilistic graphical models
    • Nave Bayes classifiers
      • Introducing the multinomial Nave Bayes
        • Formalism
        • The frequentist perspective
        • The predictive model
        • The zero-frequency problem
      • Implementation
        • Design
        • Training
          • Class likelihood
          • Binomial model
          • Multinomial model
          • Classifier components
        • Classification
        • F1 Validation
        • Features extraction
        • Testing
    • Multivariate Bernoulli classification
      • Model
      • Implementation
    • Nave Bayes and text mining
      • Basics information retrieval
      • Implementation
        • Analyzing documents
        • Extracting relative terms frequency
        • Generating the features
      • Testing
        • Retrieving textual information
        • Evaluating text mining classifier
    • Pros and cons
    • Summary
  • 7. Sequential Data Models
    • Markov decision processes
      • The Markov property
      • The first-order discrete Markov chain
    • The hidden Markov model (HMM)
      • Notation
      • The lambda model
      • Design
      • Evaluation (CF-1)
        • Alpha (forward pass)
        • Beta (backward pass)
      • Training (CF-2)
        • Baum-Welch estimator (EM)
      • Decoding (CF-3)
        • The Viterbi algorithm
      • Putting it all together
      • Test case 1 Training
      • Test case 2 Evaluation
      • HMM as filtering technique
    • Conditional random fields
      • Introduction to CRF
      • Linear chain CRF
    • Regularized CRF and text analytics
      • The feature functions model
      • Design
      • Implementation
        • Configuring the CRF classifier
        • Training the CRF model
        • Applying the CRF model
      • Tests
        • The training convergence profile
        • Impact of the size of the training set
        • Impact of L2 regularization factor
    • Comparing CRF and HMM
    • Performance consideration
    • Summary
  • 8. Monte Carlo Inference
    • The purpose of sampling
    • Gaussian sampling
      • Box-Muller transform
    • Monte Carlo approximation
      • Overview
      • Implementation
    • Bootstrapping with replacement
      • Overview
      • Resampling
      • Implementation
      • Pros and cons of bootstrap
    • Markov Chain Monte Carlo (MCMC)
      • Overview
      • Metropolis-Hastings (MH)
      • Implementation
      • Test
    • Summary
  • 9. Regression and Regularization
    • Linear regression
      • Univariate linear regression
        • Implementation
        • Test case
      • Ordinary least squares (OLS) regression
        • Design
        • Implementation
        • Test case 1 trending
        • Test case 2 features selection
    • Regularization
      • Ln roughness penalty
      • Ridge regression
        • Design
        • Implementation
        • Test case
    • Numerical optimization
    • Logistic regression
      • Logistic function
      • Design
      • Training workflow
        • Step 1 configuring the optimizer
        • Step 2 computing the Jacobian matrix
        • Step 3 managing the convergence of optimizer
        • Step 4 defining the least squares problem
        • Step 5 minimizing the sum of square errors
        • Test
      • Classification
    • Summary
  • 10. Multilayer Perceptron
    • Feed-forward neural networks (FFNN)
      • The biological background
      • Mathematical background
    • The multilayer perceptron (MLP)
      • Activation function
      • Network topology
      • Design
      • Configuration
      • Network components
        • Network topology
        • Input and hidden layers
        • Output layer
        • Synapses
        • Connections
        • Weights initialization
      • Model
      • Problem types (modes)
      • Online versus batch training
      • Training epoch
        • Step 1 input forward propagation
          • Computational flow
          • Error functions
          • Operating modes
          • Softmax
        • Step 2 error backpropagation
          • Weights adjustment
          • Error propagation
          • The computational model
        • Step 3 exit condition
        • Putting it all together
      • Training and classification
        • Regularization
        • Model generation
        • Fast Fisher-Yates shuffle
        • Prediction
        • Model fitness
    • Evaluation
      • Execution profile
      • Impact of learning rate
      • Impact of the momentum factor
      • Impact of the number of hidden layers
      • Test case
        • Implementation
        • Models evaluation
        • Impact of hidden layers' architecture
    • Benefits and limitations
    • Summary
  • 11. Deep Learning
    • Sparse autoencoder
      • Undercomplete autoencoder
      • Deterministic autoencoder
      • Categorization
      • Feed-forward sparse, undercomplete autoencoder
      • Sparsity updating equations
      • Implementation
    • Restricted Boltzmann Machines (RBMs)
      • Boltzmann machine
      • Binary restricted Boltzmann machines
        • Conditional probabilities
        • Sampling
        • Log-likelihood gradient
        • Contrastive divergence
        • Configuration parameters
        • Unsupervised learning
    • Convolution neural networks
      • Local receptive fields
      • Weight sharing
      • Convolution layers
      • Sub-sampling layers
      • Putting it all together
      • Summary
  • 12. Kernel Models and SVM
    • Kernel functions
      • Overview
      • Common discriminative kernels
      • Kernel monadic composition
    • The support vector machine (SVM)
      • The linear SVM
        • The separable case (hard margin)
        • The non-separable case (soft margin)
      • The nonlinear SVM
        • Max-margin classification
        • The kernel trick
      • Support vector classifier (SVC)
        • The binary SVC
          • LIBSVM
          • Design
          • Configuration parameters
            • The SVM formulation
            • The SVM kernel function
            • The SVM execution
          • Interface to LIBSVM
          • Training
          • Classification
          • C-penalty and margin
          • Kernel evaluation
          • Application to risk analysis
      • Anomaly detection with one-class SVC
      • Support vector regression (SVR)
        • Overview
        • SVR versus linear regression
    • Performance considerations
    • Summary
  • 13. Evolutionary Computing
    • Evolution
      • The origin
      • NP problems
      • Evolutionary computing
    • Genetic algorithms and machine learning
    • Genetic algorithm components
      • Encodings
        • Value encoding
        • Predicate encoding
        • Solution encoding
        • The encoding scheme
          • Flat encoding
          • Hierarchical encoding
      • Genetic operators
        • Selection
        • Crossover
        • Mutation
      • Fitness score
    • Implementation
      • Software design
      • Key components
        • Population
        • Chromosomes
        • Genes
      • Selection
      • Controlling population growth
      • GA configuration
      • Crossover
        • Population
        • Chromosomes
        • Genes
      • Mutation
        • Population
        • Chromosomes
        • Genes
      • Reproduction
      • Solver
    • GA for trading strategies
      • Definition of trading strategies
        • Trading operators
        • The cost function
        • Market signals
        • Trading strategies
        • Signal encoding
      • Test case Fall 2008 market crash
        • Creating trading strategies
        • Configuring the optimizer
        • Finding the best trading strategy
        • Tests
          • The weighted score
          • The unweighted score
    • Advantages and risks of genetic algorithms
    • Summary
  • 14. Multiarmed Bandits
    • K-armed bandit
      • Exploration-exploitation trade-offs
      • Expected cumulative regret
      • Bayesian Bernoulli bandits
      • Epsilon-greedy algorithm
    • Thompson sampling
      • Bandit context
      • Prior/posterior beta distribution
      • Implementation
      • Simulated exploration and exploitation
    • Upper bound confidence
      • Confidence interval
      • Implementation
    • Summary
  • 15. Reinforcement Learning
    • Reinforcement learning
      • Understanding the challenge
      • A solution Q-learning
        • Terminology
        • Concept
        • Value of policy
        • Bellman optimality equations
        • Temporal difference for model-free learning
        • Action-value iterative update
      • Implementation
        • Software design
        • The states and actions
        • The search space
        • The policy and action-value
        • The Q-learning components
        • The Q-learning training
        • Tail recursion to the rescue
        • Validation
        • The prediction
      • Option trading using Q-learning
        • Option property
        • Option model
        • Quantization
      • Putting it all together
      • Evaluation
      • Pros and cons of reinforcement learning
    • Learning classifier systems
      • Introduction to LCS
      • Combining learning and evolution
      • Terminology
        • Extended learning classifier systems
        • XCS components
        • Application to portfolio management
        • XCS core data
        • XCS rules
        • Covering
        • Example of implementation
        • Benefits and limitations of learning classifier systems
    • Summary
  • 16. Parallelism in Scala and Akka
    • Overview
    • Scala
      • Object creation
      • Streams
        • Memory on demand
        • Design for reusing Streams memory
      • Parallel collections
        • Processing a parallel collection
        • Benchmark framework
        • Performance evaluation
    • Scalability with Actors
      • The Actor model
      • Partitioning
      • Beyond Actors reactive programming
    • Akka
      • Master-workers
        • Messages exchange
        • Worker Actors
        • The workflow controller
        • The master Actor
        • Master with routing
        • Distributed discrete Fourier transform
        • Limitations
      • Futures
        • Blocking on futures
        • Future callbacks
        • Putting it all together
    • Summary
  • 17. Apache Spark MLlib
    • Overview
    • Apache Spark core
      • Why Spark?
      • Design principles
        • In-memory persistency
        • Laziness
        • Transforms and actions
        • Shared variables
      • Experimenting with Spark
        • Deploying Spark
        • Using Spark shell
    • MLlib library
      • Overview
      • Creating RDDs
      • K-means using MLlib
      • Tests
    • Reusable ML pipelines
      • Reusable ML transforms
        • Encoding features
        • Training the model
        • Predictive model
        • Training summary statistics
        • Validating the model
        • Grid search
      • Apache Spark and ScalaTest
    • Extending Spark
      • Kullback-Leibler divergence
      • Implementation
      • Kullback-Leibler evaluator
    • Streaming engine
      • Why streaming?
      • Batch and real-time processing
      • Architecture overview
      • Discretized streams
      • Use case continuous parsing
      • Checkpointing
    • Performance evaluation
      • Tuning parameters
      • Performance considerations
    • Pros and cons
    • Summary
  • A. Basic Concepts
    • Scala programming
      • List of libraries and tools
      • Code snippets format
      • Best practices
        • Encapsulation
        • Class constructor template
        • Companion objects versus case classes
        • Enumerations versus case classes
        • Overloading
        • Design template for immutable classifiers
      • Utility classes
        • Data extraction
        • Financial data sources
        • Documents extraction
        • DMatrix class
        • Counter
        • Monitor
    • Mathematics
      • Linear algebra
        • QR decomposition
        • LU factorization
        • LDL decomposition
        • Cholesky factorization
        • Singular Value Decomposition (SVD)
        • Eigenvalue decomposition
        • Algebraic and numerical libraries
      • First order predicate logic
      • Jacobian and Hessian matrices
      • Summary of optimization techniques
        • Gradient descent methods
          • Steepest descent
          • Conjugate gradient
          • Stochastic gradient descent
        • Quasi-Newton algorithms
          • BFGS
          • L-BFGS
        • Nonlinear least squares minimization
          • Gauss-Newton
          • Levenberg-Marquardt
        • Lagrange multipliers
      • Overview dynamic programming
    • Finances 101
      • Fundamental analysis
      • Technical analysis
        • Terminology
        • Trading data
        • Trading signal and strategy
        • Price patterns
      • Options trading
      • Financial data sources
    • Suggested online courses
    • References
  • B. References
    • Chapter 1
    • Chapter 2
    • Chapter 3
    • Chapter 4
    • Chapter 5
    • Chapter 6
    • Chapter 7
    • Chapter 8
    • Chapter 9
    • Chapter 10
    • Chapter 11
    • Chapter 12
    • Chapter 13
    • Chapter 14
    • Chapter 15
    • Chapter 16
    • Chapter 17
  • Index

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