Scala 机器学习库

自然语言处理

ScalaNLP—机器学习和数值计算库的套装


Breeze —Scala用的数值处理库


Chalk—自然语言处理库。


FACTORIE—可部署的概率建模工具包,用Scala实现的软件库。为用户提供简洁的语言来创建关系因素图,评估参数并进行推断。


数据分析/数据可视化

MLlib in Apache Spark—Spark下的分布式机器学习库


Scalding —CAscading的Scala接口


Summing Bird—用Scalding 和 Storm进行Streaming MapReduce


Algebird —Scala的抽象代数工具


xerial —Scala的数据管理工具


simmer —化简你的数据,进行代数聚合的unix过滤器


PredictionIO —供软件开发者和数据工程师用的机器学习服务器。


BIDMat—支持大规模探索性数据分析的CPU和GPU加速矩阵库。


通用机器学习

Conjecture—Scalding下可扩展的机器学习框架


brushfire—scalding下的决策树工具。


ganitha —基于scalding的机器学习程序库


adam—使用Apache Avro, Apache Spark 和 Parquet的基因组处理引擎,有专用的文件格式,Apache 2软件许可。


bioscala —Scala语言可用的生物信息学程序库


BIDMach—机器学习CPU和GPU加速库。


Figaro - 一个构造概率性模型的Scala库


       英文原文链接:Scala机器学习

Scala:Applied Machine Learning by Pascal Bugnion English | 23 Feb. 2017 | ISBN-13: 9781787126640 | 1843 Pages | EPUB/PDF (conv) | 33.15 MB Leverage the power of Scala and master the art of building, improving, and validating scalable machine learning and AI applications using Scala's most advanced and finest features. About This Book Build functional, type-safe routines to interact with relational and NoSQL databases with the help of the tutorials and examples provided Leverage your expertise in Scala programming to create and customize your own scalable machine learning algorithms Experiment with different techniques; evaluate their benefits and limitations using real-world financial applications Get to know the best practices to incorporate new Big Data machine learning in your data-driven enterprise and gain future scalability and maintainability Who This Book Is For This Learning Path is for engineers and scientists who are familiar with Scala and want to learn how to create, validate, and apply machine learning algorithms. It will also benefit software developers with a background in Scala programming who want to apply machine learning. What You Will Learn Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters Apply key learning strategies to perform technical analysis of financial markets Understand the principles of supervised and unsupervised learning in machine learning Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet Construct reliable and robust data pipelines and manage data in a data-driven enterprise Implement scalable model monitoring and alerts with Scala In Detail This Learning Path aims to put the entire world of machine learning with Scala in fron
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