更新时间：2021-07-08 10:43:39
封面
版权页
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
Chapter 3. Data Preprocessing
Time series in Scala
Moving averages
Fourier analysis
The discrete Kalman filter
Alternative preprocessing techniques
Chapter 4. Unsupervised Learning
K-mean clustering
Expectation-Maximization (EM)
Chapter 5. Dimension Reduction
Challenging model complexity
The divergences
Principal components analysis (PCA)
Nonlinear models
Chapter 6. Naïve Bayes Classifiers
Probabilistic graphical models
Naïve Bayes classifiers
Multivariate Bernoulli classification
Naïve Bayes and text mining
Pros and cons
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
Chapter 8. Monte Carlo Inference
The purpose of sampling
Gaussian sampling
Monte Carlo approximation
Bootstrapping with replacement
Markov Chain Monte Carlo (MCMC)
Chapter 9. Regression and Regularization
Linear regression
Regularization
Numerical optimization
Logistic regression
Chapter 10. Multilayer Perceptron
Feed-forward neural networks (FFNN)
The multilayer perceptron (MLP)
Evaluation
Benefits and limitations
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
Chapter 13. Evolutionary Computing
Evolution
Genetic algorithms and machine learning
Genetic algorithm components
Implementation
GA for trading strategies
