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Scala for Machine Learning（Second Edition）

Patrick R. Nicolas 著

更新時(shí)間：2021-07-08 10:43:39

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計(jì)算機(jī)網(wǎng)絡(luò) 編程語(yǔ)言與程序設(shè)計(jì)

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目錄(152章)

倒序

封面
版權(quán)頁(yè)
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(shí)間：2021-07-08 10:43:39

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Scala for Machine Learning（Second Edition）