Showing 6 open source projects for "basic programming software"

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  • 1
    Java Neural Network Framework Neuroph
    Neuroph is lightweight Java Neural Network Framework which can be used to develop common neural network architectures. Small number of basic classes which correspond to basic NN concepts, and GUI editor makes it easy to learn and use.
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    Downloads: 19 This Week
    Last Update:
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  • 2
    Neural Libs

    Neural Libs

    Neural network library for developers

    This project includes the implementation of a neural network MLP, RBF, SOM and Hopfield networks in several popular programming languages. The project also includes examples of the use of neural networks as function approximation and time series prediction. Includes a special program makes it easy to test neural network based on training data and the optimization of the network.
    Downloads: 5 This Week
    Last Update:
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  • 3

    Fast Matrix for Java

    General purpose matrix utilities for Java in Parallel Computing

    Fast Matrix for Java (fm4j) is a general-purpose matrix utility library for computing with dense matrices. fm4j encapsulated different underlying implementations and select the optimal one in run-time depending on the size of the input matrix. Moreover, fm4j employs Java (Tm) Concurrency to take advantage of the computation power of multi-cor processors.
    Downloads: 0 This Week
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  • 4

    cognity

    A neural network library for Java.

    Cognity is an object-oriented neural network library for Java. It's goal is to provide easy-to-use, high level architecture for neural network computations along with reasonable performance.
    Downloads: 0 This Week
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  • 5
    ANNJ, Another Neural Network for Java is a neural network framework for the Java programming language. It is still in an early development stage, currently supporting only feed-forward type networks, but will soon be able to handle many other types.
    Downloads: 0 This Week
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  • 6
    nn-utility is a neural network library for C++ and Java. Its aim is to simplify the tedious programming of neural networks, while allowing programmers to have maximum flexibility in terms of defining functions and network topology.
    Downloads: 0 This Week
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