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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

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781787122383

Length 740 pages

Edition 2nd Edition

Languages

Scala

Tools

Apache Spark

Concepts

Data Processing

Author (1):

Patrick R. Nicolas

View More author details

Table of Contents (21) Chapters

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

Summary

The multilayer perceptron is a non-parametric estimator that has been used for several decades in various industries, from insurance to image processing. It has been proven a reliable model for classification at the cost of lengthy execution.

In this chapter, you learned the origin of feedforward neural networks, how to implement the different steps of the training cycle with backpropagation in Scala, experiment with the different configuration parameters and apply the multilayer perceptron to the analysis of the fluctuations in the currencies market.

The multilayer perceptron, along with Monte Carlo sampling (Chapter 8, Monte Carlo Inference) and regularization (Chapter 9, Regression and Regularization), is a key ingredient of deep learning, which is the topic of the next chapter.

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Authors (1)

R. Nicolas

R. Nicolas

Patrick R. Nicolas is the director of engineering at Agile SDE, California. He has more than 25 years of experience in software engineering and building applications in C++, Java, and more recently in Scala/Spark, and has held several managerial positions. His interests include real-time analytics, modeling, and the development of nonlinear models.

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