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Explore the powerful combination of functional programming and Scala for machine learning in this 34-minute conference talk from YOW! 2015. Discover how Scala's growing ecosystem of machine learning and natural language processing tools offers a compelling alternative to traditional Python-based frameworks. Learn about the advantages of writing strongly typed functional code for mathematical computations and simulations. Gain insights into Scala's machine learning ecosystem, including numeric processing libraries like Spire and Breeze, big data machine learning tools such as MLLib and Mahout, and GPU-based text parsing with Puck. Follow along as the speaker demonstrates how to build a scalable algorithm implementation by reusing existing components, showcasing a vectorized linear regression example that runs native BLAS code and can handle petabytes of data using Spark.
