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Scala for Machine Learning, Second Edition

Scala for Machine Learning, Second Edition

By : R. Nicolas
4.5 (2)
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Scala for Machine Learning, Second Edition

Scala for Machine Learning, Second Edition

4.5 (2)
By: R. Nicolas

Overview of this book

The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, engineering design, logistics, manufacturing, and trading strategies, to detection of genetic anomalies. The book is your one stop guide that introduces you to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits. You start by learning data preprocessing and filtering techniques. Following this, you'll move on to unsupervised learning techniques such as clustering and dimension reduction, followed by probabilistic graphical models such as Naïve Bayes, hidden Markov models and Monte Carlo inference. Further, it covers the discriminative algorithms such as linear, logistic regression with regularization, kernelization, support vector machines, neural networks, and deep learning. You’ll move on to evolutionary computing, multibandit algorithms, and reinforcement learning. Finally, the book includes a comprehensive overview of parallel computing in Scala and Akka followed by a description of Apache Spark and its ML library. With updated codes based on the latest version of Scala and comprehensive examples, this book will ensure that you have more than just a solid fundamental knowledge in machine learning with Scala.
Table of Contents (21 chapters)
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20
Index

Advantages and risks of genetic algorithms


It should be clear by now that genetic algorithms provide scientists with a powerful optimization tool for problems that:

  • Are poorly understood.

  • May have more than one good enough solution.

  • Have discrete, discontinuous, and non-differentiable functions.

  • Can be easily integrated with the rules or policies engine (see the Learning classifiers systems section in Chapter 15, Reinforcement Learning).

  • Do not require deep domain knowledge. The genetic algorithm generates new solution candidates through genetic operators without the need to specify constraints and initial conditions.

  • Do not require knowledge of numerical methods such as the Newton-Raphson, conjugate gradient, or L-BFGS as optimization techniques, which frighten those with little inclination for mathematics.

However, evolutionary computation is not suitable for problems for which:

  • A fitness or scoring function cannot be quantified or even defined

  • There is a need to find the global minimum or maximum...

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