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Scala for Machine Learning, Second Edition

You're reading from   Scala for Machine Learning, Second Edition Build systems for data processing, machine learning, and deep learning

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787122383
Length 740 pages
Edition 2nd Edition
Languages
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Author (1):
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Patrick R. Nicolas Patrick R. Nicolas
Author Profile Icon Patrick R. Nicolas
Patrick R. Nicolas
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Table of Contents (21) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Data Pipelines 3. Data Preprocessing 4. Unsupervised Learning 5. Dimension Reduction 6. Naïve Bayes Classifiers 7. Sequential Data Models 8. Monte Carlo Inference 9. Regression and Regularization 10. Multilayer Perceptron 11. Deep Learning 12. Kernel Models and SVM 13. Evolutionary Computing 14. Multiarmed Bandits 15. Reinforcement Learning 16. Parallelism in Scala and Akka 17. Apache Spark MLlib A. Basic Concepts B. References Index

Fourier analysis

The purpose of spectral density estimation is to measure the amplitude of a signal or a time series according to its frequency [3:5]. The objective is to estimate the spectral density by detecting periodicities in the dataset. A scientist can better understand a signal or time series by analyzing its harmonics.

Note

Spectral theory:

Spectral analysis for time series should not be confused with Spectral Theory, a subset of linear algebra that studies Eigen functions on Hilbert and Banach spaces. In fact, harmonic analysis and Fourier analysis are regarded as a subset of spectral theory.

Let us explore the concept behind the discrete Fourier series as well as its benefits as applied to financial markets. Fourier analysis approximates any generic function as the sum of trigonometric functions, sine and cosine.

Note

Complex Fourier transform:

This section focuses on the discrete Fourier series for real value. The generic Fourier transform applies to complex values [3:6].

The decomposition...

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