Showing 10 open source projects for "fast linux"

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  • 1
    Smile

    Smile

    Statistical machine intelligence and learning engine

    Smile is a fast and comprehensive machine learning engine. With advanced data structures and algorithms, Smile delivers the state-of-art performance. Compared to this third-party benchmark, Smile outperforms R, Python, Spark, H2O, xgboost significantly. Smile is a couple of times faster than the closest competitor. The memory usage is also very efficient. If we can train advanced machine learning models on a PC, why buy a cluster? Write applications quickly in Java, Scala, or any JVM...
    Downloads: 0 This Week
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  • 2
    ADAMS

    ADAMS

    ADAMS is a workflow engine for building complex knowledge workflows.

    ADAMS is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes. Instead of placing operators on a canvas and manually connecting them, a tree structure and flow control operators determine how data is processed (sequentially/parallel). This allows rapid development and easy maintenance of large workflows, with hundreds or thousands of operators. Operators include machine learning (WEKA, MOA, MEKA)...
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    Downloads: 7 This Week
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  • 3
    exchange-core

    exchange-core

    Ultra-fast matching engine written in Java based on LMAX Disruptor

    Exchange-core is an open-source market exchange core based on LMAX Disruptor, Eclipse Collections (ex. Goldman Sachs GS Collections), Real Logic Agrona, OpenHFT Chronicle-Wire, LZ4 Java, and Adaptive Radix Trees. Designed for high scalability and pauseless 24/7 operation under high-load conditions and providing low-latency responses. Single order book configuration is capable to process 5M operations per second on 10-years old hardware (Intel® Xeon® X5690) with moderate latency degradation....
    Downloads: 0 This Week
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  • 4
    H2O-3

    H2O-3

    H2O is an Open Source, Distributed, Fast & Scalable Machine Learning

    H2O-3 is an open-source machine learning platform designed to build scalable and distributed machine learning models across large datasets. The system operates as an in-memory computing platform that allows data scientists to train models quickly using distributed resources. It supports many machine learning algorithms including generalized linear models, gradient boosting machines, deep learning networks, and ensemble techniques. The platform provides interfaces for multiple programming...
    Downloads: 0 This Week
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  • 5

    LAML:Linear Algebra and Machine Learning

    A stand-alone Java library for linear algebra and machine learning

    LAML is a stand-alone pure Java library for linear algebra and machine learning. The goal is to build efficient and easy-to-use linear algebra and machine learning libraries. The reason why linear algebra and machine learning are built together is that full control of the basic data structures for matrices and vectors is required to have fast implementation for machine learning methods. Additionally, LAML provides a lot of commonly used matrix functions in the same signature to MATLAB, thus...
    Downloads: 0 This Week
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  • 6

    FENNIX

    Fast EXperimentation with Neural Networks

    FENNIX is a simulator of artificial neural networks written in Java. It allows you to easily describe a complete simulation by using a simple text script language or by adding nodes to a tree of tasks by using the graphical used interface. Moreover, FENNIX is composed of pluggable tools that can be easily modified in order to add new functionalities to the simulator.
    Downloads: 0 This Week
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  • 7
    Clustering Variation looks for a good subset of attributes in order to improve the classification accuracy of supervised learning techniques in classification problems with a huge number of attributes involved. It first creates a ranking of attributes based on the Variation value, then divide into two groups, last using Verification method to select the best group.
    Downloads: 13 This Week
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  • 8
    TreeLiker

    TreeLiker

    TreeLiker is a collection of fast algorithms for working with complex

    TreeLiker is a collection of fast algorithms for working with complex structured data in relational form. The data can, for example, describe large organic molecules such as proteins or groups of individuals such as social networks or predator-prey networks etc. The algorithms included in TreeLiker are unique in that, in principle, they are able to search given sets of relational patterns exhaustively, thus guaranteeing that if some good pattern capturing an important feature of the problem...
    Downloads: 0 This Week
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  • 9
    Nen

    Nen

    neural network implementation in java

    3-layer neural network for regression and classification with sigmoid activation function and command line interface similar to LibSVM. Quick Start: "java -jar nen.jar"
    Downloads: 0 This Week
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  • 10
    A general purpose Finite State Machine written in Java. It is easy to use, powerful, and fast.
    Downloads: 0 This Week
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