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Scala for machine learning : data processing, ML algorithms, smart analytics, and more /
Leverage Scala and Machine Learning to study and construct systems that can learn from data About This Book Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulation, and updated source code in Scala Take your expertise in Scala p...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2017.
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| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a Nicolas, Patrick R., |e author. | |
| 245 | 1 | 0 | |a Scala for machine learning : |b data processing, ML algorithms, smart analytics, and more / |c Patrick R. Nicolas. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2017. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from cover (viewed October 23, 2017). | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a Leverage Scala and Machine Learning to study and construct systems that can learn from data About This Book Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulation, and updated source code in Scala Take your expertise in Scala programming to the next level by creating and customizing AI applications Experiment with different techniques and evaluate their benefits and limitations using real-world applications in a tutorial style Who This Book Is For If you're a data scientist or a data analyst with a fundamental knowledge of Scala who wants to learn and implement various Machine learning techniques, this book is for you. 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! What You Will Learn Build dynamic workflows for scientific computing Leverage open source libraries to extract patterns from time series Write your own classification, clustering, or evolutionary algorithm Perform relative performance tuning and evaluation of Spark Master probabilistic models for sequential data Experiment with advanced techniques such as regularization and kernelization Dive into neural networks and some deep learning architecture Apply some basic multiarm-bandit algorithms Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters Apply key learning strategies to a technical analysis of financial markets In Detail 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, s... | ||
| 505 | 0 | |a Scala for machine learning : data processing, ML algorithms, smart analytics, and more, second edition -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting Started -- Chapter 2: Data Pipelines -- Chapter 3: Data Preprocessing -- Chapter 4: Unsupervised Learning -- Chapter 5: Dimension Reduction -- Chapter 6: Naïve Bayes Classifiers -- Chapter 7: Sequential Data Models -- Chapter 8: Monte Carlo Inference -- Chapter 9: Regression and Regularization -- Chapter 10: Multilayer Perceptron -- Chapter 11: Deep Learning -- Chapter 12: Kernel Models and SVM -- Chapter 13: Evolutionary Computing -- Chapter 14: Multiarmed Bandits -- Chapter 15: Reinforcement Learning -- Chapter 16: Parallelism in Scala and Akka -- Chapter 17: Apache Spark MLlib -- Appendix A: Basic Concepts -- Appendix B: References. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Scala (Computer program language) | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Regression analysis |x Data processing. | |
| 650 | 6 | |a Scala (Langage de programmation) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Analyse de régression |x Informatique. | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Regression analysis |x Data processing. |2 fast |0 (OCoLC)fst01093274 | |
| 650 | 7 | |a Scala (Computer program language) |2 fast |0 (OCoLC)fst01763491 | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781787122383/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a ProQuest MyiLibrary Digital eBook Collection |b IDEB |n cis39015986 | ||
| 994 | |a 92 |b IZTAP | ||