Scala for Machine Learning - Second Edition: Build systems for data processing, machine learning, and deep learning

Description

Key FeaturesExplore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulation, and updated source code in ScalaTake your expertise in Scala programming to the next level by creating and customizing AI applicationsExperiment with different techniques and evaluate their benefits and limitations using real-world applications in a tutorial styleBook DescriptionThe 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 Naive 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.What you will learnBuild dynamic workflows for scientific computingLeverage open source libraries to extract patterns from time seriesWrite your own classification, clustering, or evolutionary algorithmPerform relative performance tuning and evaluation of SparkMaster probabilistic models for sequential dataExperiment with advanced techniques such as regularization and kernelizationDive into neural networks and some deep learning architectureApply some basic multiarm-bandit algorithmsSolve big data problems with Scala parallel collections, Akka actors, and Apache Spark clustersApply key learning strategies to a technical analysis of financial marketsAbout the AuthorPatrick 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. Table of ContentsGetting Started Data pipeline Data pre-processing Clustering Dimension reduction Naive Bayes Classifiers Sequential data models Monte Carlo Inference Regression and Regularization Multi-layer perceptron Deep learning Kernel models & support vector machines Evolutionary computing Multi-arm bandits Reinforcement learning Parallelism in Scala and Akka Apache Spark Appendix Basic concepts References