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Scala for Machine Learning

Scala for Machine Learning

By : R. Nicolas
3.8 (12)
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Scala for Machine Learning

Scala for Machine Learning

3.8 (12)
By: R. Nicolas

Overview of this book

Are you curious about AI? All you need is a good understanding of the Scala programming language, a basic knowledge of statistics, a keen interest in Big Data processing, and this book!
Table of Contents (15 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Getting Started
1. Getting Started
Mathematical notation for the curious
Why machine learning?
Why Scala?
Model categorization
Taxonomy of machine learning algorithms
Tools and frameworks
Source code
Let's kick the tires
Summary
2
2. Hello World!
2. Hello World!
Modeling
Designing a workflow
Assessing a model
Summary
3
3. Data Preprocessing
3. Data Preprocessing
Time series
Moving averages
Fourier analysis
The Kalman filter
Alternative preprocessing techniques
Summary
4
4. Unsupervised Learning
4. Unsupervised Learning
Clustering
Dimension reduction
Performance considerations
Summary
5
5. Naïve Bayes Classifiers
5. Naïve Bayes Classifiers
Probabilistic graphical models
Naïve Bayes classifiers
Multivariate Bernoulli classification
Naïve Bayes and text mining
Pros and cons
Summary
6
6. Regression and Regularization
6. Regression and Regularization
Linear regression
Regularization
Numerical optimization
The logistic regression
Summary
7
7. Sequential Data Models
7. Sequential Data Models
Markov decision processes
The hidden Markov model (HMM)
Conditional random fields
CRF and text analytics
Comparing CRF and HMM
Performance consideration
Summary
8
8. Kernel Models and Support Vector Machines
8. Kernel Models and Support Vector Machines
Kernel functions
The support vector machine (SVM)
Support vector classifier (SVC)
Anomaly detection with one-class SVC
Support vector regression (SVR)
Performance considerations
Summary
9
9. Artificial Neural Networks
9. Artificial Neural Networks
Feed-forward neural networks (FFNN)
The multilayer perceptron (MLP)
Evaluation
Benefits and limitations
Summary
10
10. Genetic Algorithms
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10. Genetic Algorithms
Evolution
Genetic algorithms and machine learning
Genetic algorithm components
Implementation
GA for trading strategies
Advantages and risks of genetic algorithms
Summary
11
11. Reinforcement Learning
11. Reinforcement Learning
Introduction
Learning classifier systems
Summary
12
12. Scalable Frameworks
12. Scalable Frameworks
Overview
Scala
Scalability with Actors
Akka
Apache Spark
Summary
13
A. Basic Concepts
A. Basic Concepts
Scala programming
Mathematics
Finances 101
Suggested online courses
References
14
Index
Index

Chapter 10. Genetic Algorithms

This chapter introduces the concept of evolutionary computing. Algorithms derived from the theory of evolution are particularly efficient in solving large combinatorial or NP problems. Evolutionary computing has been pioneered by John Holland [10:1] and David Goldberg [10:2]. Their findings should be of interest to anyone eager to learn about the foundation of genetic algorithms (GA) and artificial life.

This chapter covers the following topics:

  • The origin of evolutionary computing
  • The theoretical foundation of genetic algorithms
  • Advantages and limitations of genetic algorithms

From a practical perspective, you will learn how to:

  • Apply genetic algorithms to leverage technical analysis of market price and volume movement to predict future returns
  • Evaluate or estimate the search space
  • Encode solutions in the binary format using either hierarchical or flat addressing
  • Tune some of the genetic operators
  • Create and evaluate fitness functions

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