JRuby Mahout
JRuby Mahout is a gem that enables users to use Apache Mahout in their Ruby projects. subtitle
JRuby Mahout Alternatives
The best JRuby Mahout alternatives based on verified products, community votes, reviews and other factors.
Latest update:
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/scikit-learn-alternatives
scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
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/numpy-alternatives
NumPy is the fundamental package for scientific computing with Python
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/opencv-alternatives
OpenCV is the world's biggest computer vision library
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/htm-java-alternatives
htm.java is a Hierarchical Temporal Memory implementation in Java, it provide a Java version of NuPIC that has a 1-to-1 correspondence to all systems, functionality and tests provided by Numenta's open source implementation.
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/dataiku-alternatives
Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.
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/pandas-alternatives
Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
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/exploratory-alternatives
Exploratory enables users to understand data by transforming, visualizing, and applying advanced statistics and machine learning algorithms.
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/figure-eight-alternatives
Figure Eight is the essential Human-in-the-Loop Machine Learning platform.
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/weka-alternatives
WEKA is a set of powerful data mining tools that run on Java.
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/turi-graphlab-create-alternatives
GraphLab Create is an extensible machine learning framework that enables developers and data scientists to easily build and deploy apps.
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/dimml-alternatives
The DimML programming language enables users to run any data solution on any website with only a single line of code.
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/logical-glue-alternatives
Logical Glue helps Lenders and Insurance organisations make better decisions with a highly intuitive and user-friendly Machine Learning Platform.
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/rapidminer-alternatives
RapidMiner is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.