Open Source C++ Neural Network Libraries for Windows

Browse free open source C++ Neural Network Libraries for Windows and projects below. Use the toggles on the left to filter open source C++ Neural Network Libraries for Windows by OS, license, language, programming language, and project status.

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  • 1
    Simd

    Simd

    High performance image processing library in C++

    The Simd Library is a free open source image processing library, designed for C and C++ programmers. It provides many useful high performance algorithms for image processing such as: pixel format conversion, image scaling and filtration, extraction of statistic information from images, motion detection, object detection (HAAR and LBP classifier cascades) and classification, neural network. The algorithms are optimized with using of different SIMD CPU extensions. In particular the library supports following CPU extensions: SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2 and AVX-512 for x86/x64, VMX(Altivec) and VSX(Power7) for PowerPC, NEON for ARM. The Simd Library has C API and also contains useful C++ classes and functions to facilitate access to C API. The library supports dynamic and static linking, 32-bit and 64-bit Windows, Android and Linux, MSVS, G++ and Clang compilers, MSVS project and CMake build systems.
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    Downloads: 20 This Week
    Last Update:
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  • 2
    Neural network libraries in many different languages compatible with each other, such that neural networks can be trained on one platform and utilised on another.
    Downloads: 2 This Week
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  • 3
    OpenAIL

    OpenAIL

    Open Artificial Intelligence Library

    [Open Artificial Intelligence Library]. This library main goal is to provide a tool box to those who want to use algorithm such as neural network or genetics Algorithm and all algorithms that are commonly within the Artificial Intelligence field.
    Downloads: 0 This Week
    Last Update:
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  • 4
    Syntactic is a simple C++ library for constructing general Neural networks. For now the library supports creation of multi layered networks for the Feedforward Backpropagation algorithm as well as Time Series Networks. More will be implemented soon.
    Downloads: 0 This Week
    Last Update:
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