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Scala for Machine Learning（Second Edition）
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Index
Ifyou’readatascientistoradataanalystwithafundamentalknowledgeofScalawhowantstolearnandimplementvariousMachinelearningtechniques,thisbookisforyou.AllyouneedisagoodunderstandingoftheScalaprogramminglanguage,abasicknowledgeofstatistics,akeeninterestinBigDataprocessing,andthisbook!
目錄(152章)
倒序
- 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
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