Showing 5 open source projects for "evolution linux"

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    Paessler: Easy to Use With Enterprise Power. Free Trial

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  • 1
    DeepCTR-Torch

    DeepCTR-Torch

    Easy-to-use,Modular and Extendible package of deep-learning models

    DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical...
    Downloads: 6 This Week
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  • 2
    CASUP

    CASUP

    Cellular Automata library for SUPercomputers (CASUP)

    CASUP is a Cellular Automata (CA) library for HPC and supercomputers. It can be used in materials science (microstructure evolution, grain coarsening, fracture etc.), physics (Ising magnetisation) and for many other CA. If you use the library, please reference it as: A. Shterenlikht, L. Margetts, Three-dimensional cellular automata modelling of cleavage propagation across crystal boundaries in polycrystalline microstructures, Proc. Roy. Soc. A 471:20150039, DOI:...
    Downloads: 0 This Week
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  • 3

    EMFTrace

    Repository for dependencies between software design artefacts

    EMFTrace extends the EMFStore repository by elicitating dependencies between related models of different modeling and programming languages. The explicit recording and modeling of different types of dependencies as traceability links shall support evolutionary changes by impact analysis, early evaluation of quality flaws, and better comprehension. It is an open source project to support the practical application of research results regarding development methods for evolution of software...
    Downloads: 4 This Week
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  • 4
    This project provides a set of Python tools for creating various kinds of neural networks, which can also be powered by genetic algorithms using grammatical evolution. MLP, backpropagation, recurrent, sparse, and skip-layer networks are supported.
    Downloads: 0 This Week
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    Ship Agents Faster

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  • 5
    The objective of this project is to provide the benefits of object-oriented modeling in projects developed using structured analysis and which will still be active for many years and require maintenance evolution.
    Downloads: 0 This Week
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