Scala for Machine Learning
- نوع فایل : کتاب
- زبان : انگلیسی
- نویسنده : Patrick R. Nicolas
- چاپ و سال / کشور: 2014
- تعداد صفحه : 520
- شابک / ISBN : 1783558741
Description
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 designs, biometrics, and trading strategies, to detection of genetic anomalies.
The book begins with an introduction 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.
Next, you’ll learn about data preprocessing and filtering techniques. Following this, you’ll move on to clustering and dimension reduction, Naive Bayes, regression models, sequential data, regularization and kernelization, support vector machines, neural networks, generic algorithms, and re-enforcement learning. A review of the Akka framework and Apache Spark clusters concludes the tutorial.
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
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
The book begins with an introduction 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.
Next, you’ll learn about data preprocessing and filtering techniques. Following this, you’ll move on to clustering and dimension reduction, Naive Bayes, regression models, sequential data, regularization and kernelization, support vector machines, neural networks, generic algorithms, and re-enforcement learning. A review of the Akka framework and Apache Spark clusters concludes the tutorial.
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
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
