最新章節

• Index
• Chapter 17
• Chapter 16
• Chapter 15
• Chapter 14
• Chapter 13

• Index 更新時間：2021-07-08 10:43:39
• Chapter 17
• Chapter 16
• Chapter 15
• Chapter 14
• Chapter 13
• Chapter 12
• Chapter 11
• Chapter 10
• Chapter 9
• Chapter 8
• Chapter 7
• Chapter 6
• Chapter 5
• Chapter 4
• Chapter 3
• Chapter 2
• Chapter 1
• Appendix B. References
• References
• Suggested online courses
• Finances 101
• Mathematics
• Scala programming
• Appendix A. Basic Concepts
• Summary
• Pros and cons
• Performance evaluation
• Streaming engine
• Extending Spark
• Reusable ML pipelines
• MLlib library
• Apache Spark core
• Overview
• Chapter 17. Apache Spark MLlib
• Summary
• Akka
• Scalability with Actors
• Scala
• Overview
• Chapter 16. Parallelism in Scala and Akka
• Summary
• Learning classifier systems
• Reinforcement learning
• Chapter 15. Reinforcement Learning
• Summary
• Upper bound confidence
• Thompson sampling
• K-armed bandit
• Chapter 14. Multiarmed Bandits
• Summary
• Advantages and risks of genetic algorithms
• GA for trading strategies
• Implementation
• Genetic algorithm components
• Genetic algorithms and machine learning
• Evolution
• Chapter 13. Evolutionary Computing
• Summary
• Performance considerations
• The support vector machine (SVM)
• Kernel functions
• Chapter 12. Kernel Models and SVM
• Convolution neural networks
• Restricted Boltzmann Machines (RBMs)
• Sparse autoencoder
• Chapter 11. Deep Learning
• Summary
• Benefits and limitations
• Evaluation
• The multilayer perceptron (MLP)
• Feed-forward neural networks (FFNN)
• Chapter 10. Multilayer Perceptron
• Summary
• Logistic regression
• Numerical optimization
• Regularization
• Linear regression
• Chapter 9. Regression and Regularization
• Summary
• Markov Chain Monte Carlo (MCMC)
• Bootstrapping with replacement
• Monte Carlo approximation
• Gaussian sampling
• The purpose of sampling
• Chapter 8. Monte Carlo Inference
• Summary
• Performance consideration
• Comparing CRF and HMM
• Regularized CRF and text analytics
• Conditional random fields
• The hidden Markov model (HMM)
• Markov decision processes
• Chapter 7. Sequential Data Models
• Summary
• Pros and cons
• Na?ve Bayes and text mining
• Multivariate Bernoulli classification
• Na?ve Bayes classifiers
• Probabilistic graphical models
• Chapter 6. Na?ve Bayes Classifiers
• Summary
• Nonlinear models
• Principal components analysis (PCA)
• The divergences
• Challenging model complexity
• Chapter 5. Dimension Reduction
• Summary
• Expectation-Maximization (EM)
• K-mean clustering
• Chapter 4. Unsupervised Learning
• Summary
• Alternative preprocessing techniques
• The discrete Kalman filter
• Fourier analysis
• Moving averages
• Time series in Scala
• Chapter 3. Data Preprocessing
• Summary
• Assessing a model
• Profiling data
• Workflow computational model
• Monadic data transformation
• Defining a methodology
• Modeling
• Chapter 2. Data Pipelines
• Summary
• Let's kick the tires
• Source code
• Tools and frameworks
• Leveraging Java libraries
• Taxonomy of machine learning algorithms
• Model categorization
• Why Scala?
• Why machine learning?
• Mathematical notations for the curious
• Chapter 1. Getting Started
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Customer Feedback
• eBooks discount offers and more
• www.PacktPub.com
• About the Reviewers
• About the Author
• Credits
• 版權頁
• 封面

• 封面
• 版權頁
• Credits
• About the Author
• About the Reviewers
• www.PacktPub.com
• eBooks discount offers and more
• Customer Feedback
• Preface
• What this book covers
• What you need for this book
• Who this book is for
• Conventions
• Reader feedback
• Customer support
• Chapter 1. Getting Started
• Mathematical notations for the curious
• Why machine learning?
• Why Scala?
• Model categorization
• Taxonomy of machine learning algorithms
• Leveraging Java libraries
• Tools and frameworks
• Source code
• Let's kick the tires
• Summary
• Chapter 2. Data Pipelines
• Modeling
• Defining a methodology
• Monadic data transformation
• Workflow computational model
• Profiling data
• Assessing a model
• Summary
• Chapter 3. Data Preprocessing
• Time series in Scala
• Moving averages
• Fourier analysis
• The discrete Kalman filter
• Alternative preprocessing techniques
• Summary
• Chapter 4. Unsupervised Learning
• K-mean clustering
• Expectation-Maximization (EM)
• Summary
• Chapter 5. Dimension Reduction
• Challenging model complexity
• The divergences
• Principal components analysis (PCA)
• Nonlinear models
• Summary
• Chapter 6. Na?ve Bayes Classifiers
• Probabilistic graphical models
• Na?ve Bayes classifiers
• Multivariate Bernoulli classification
• Na?ve Bayes and text mining
• Pros and cons
• Summary
• Chapter 7. Sequential Data Models
• Markov decision processes
• The hidden Markov model (HMM)
• Conditional random fields
• Regularized CRF and text analytics
• Comparing CRF and HMM
• Performance consideration
• Summary
• Chapter 8. Monte Carlo Inference
• The purpose of sampling
• Gaussian sampling
• Monte Carlo approximation
• Bootstrapping with replacement
• Markov Chain Monte Carlo (MCMC)
• Summary
• Chapter 9. Regression and Regularization
• Linear regression
• Regularization
• Numerical optimization
• Logistic regression
• Summary
• Chapter 10. Multilayer Perceptron
• Feed-forward neural networks (FFNN)
• The multilayer perceptron (MLP)
• Evaluation
• Benefits and limitations
• Summary
• Chapter 11. Deep Learning
• Sparse autoencoder
• Restricted Boltzmann Machines (RBMs)
• Convolution neural networks
• Chapter 12. Kernel Models and SVM
• Kernel functions
• The support vector machine (SVM)
• Performance considerations
• Summary
• Chapter 13. Evolutionary Computing
• Evolution
• Genetic algorithms and machine learning
• Genetic algorithm components
• Implementation
• GA for trading strategies
• Advantages and risks of genetic algorithms
• Summary
• Chapter 14. Multiarmed Bandits
• K-armed bandit
• Thompson sampling
• Upper bound confidence
• Summary
• Chapter 15. Reinforcement Learning
• Reinforcement learning
• Learning classifier systems
• Summary
• Chapter 16. Parallelism in Scala and Akka
• Overview
• Scala
• Scalability with Actors
• Akka
• Summary
• Chapter 17. Apache Spark MLlib
• Overview
• Apache Spark core
• MLlib library
• Reusable ML pipelines
• Extending Spark
• Streaming engine
• Performance evaluation
• Pros and cons
• Summary
• Appendix A. Basic Concepts
• Scala programming
• Mathematics
• Finances 101
• Suggested online courses
• References
• Appendix 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 更新時間：2021-07-08 10:43:39