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Scala for Machine Learning

Scala for Machine Learning

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

Scala for Machine Learning

3.8 (12)
By: R. Nicolas
Buy this Book

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

Summary

This concludes not only the journey inside the multilayer perceptron, but also the introduction of the supervised learning algorithms. In this chapter, you learned:

  • The components and architecture of a neural networks
  • The stages of the training cycle of a backpropagation multilayer perceptron
  • How to implement an MLP from the ground up in Scala
  • The numerous configuration parameters and options to use MLP as a classifier and regression
  • To evaluate the impact of the learning rate and the gradient descent momentum factor on the convergence of the sum of squared errors during training
  • How to apply a multilayer perceptron to the financial analysis of the fluctuation of currencies

The next chapter will introduce the concept of genetic algorithms with a full implementation in Scala. Although, strictly speaking, genetic algorithms do not belong to the family of machine learning algorithms, they play a crucial role in the optimization of nonlinear, nondifferentiable problems and the selection of...

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