Showing 5 open source projects for "q-dir"

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  • 1
    PennyLane

    PennyLane

    A cross-platform Python library for differentiable programming

    ...The same quantum circuit model can be run on different devices. Install plugins to run your computational circuits on more devices, including Strawberry Fields, Amazon Braket, Qiskit and IBM Q, Google Cirq, Rigetti Forest, and the Microsoft QDK.
    Downloads: 0 This Week
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  • 2
    DeepPavlov

    DeepPavlov

    A library for deep learning end-to-end dialog systems and chatbots

    ...It has comprehensive and flexible tools that let developers and NLP researchers create production-ready conversational skills and complex multi-skill conversational assistants. Use BERT and other state-of-the-art deep learning models to solve classification, NER, Q&A and other NLP tasks. DeepPavlov Agent allows building industrial solutions with multi-skill integration via API services.
    Downloads: 0 This Week
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  • 3
    GDAL wheels for linux

    GDAL wheels for linux

    GDAL wheels for python and C/C++ projects (Linux only)

    To use precompiled wheels: 1) go to releases (Files) and download tarball needed; 2) install it with command: python3 -m pip install /path/to/wheel.whl Or simply use URL in pip: python3 -m pip install https://sourceforge.net/projects/gdal-wheels-for-linux/files/GDAL-3.1.4-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl/download URL may be found under "View details" button (i) To use GDAL in C/C++ project you need to link gdal lib AND all libs located at dir GDAL.libs (usually this folder resides inside python site-packages) To compile your own wheels see information given at forefather project: https://github.com/youngpm/gdalmanylinux Usually this is done via command `make wheels` GDAL wheels for Windows are provided by Christoph Gohlke at https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal Built with PROJ (proj.db is included), GEOS, EXPAT. ...
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    Downloads: 18 This Week
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  • 4
    TRFL

    TRFL

    TensorFlow Reinforcement Learning

    ...TRFL supports both CPU and GPU TensorFlow environments, though TensorFlow itself must be installed separately. It exposes clean, modular APIs for various RL methods including Q-learning, policy gradient, and actor-critic algorithms, among others. Each function returns not only the computed loss tensor but also a detailed structure containing auxiliary information like TD errors and targets.
    Downloads: 0 This Week
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  • 5
    Teach Me Quantum

    Teach Me Quantum

    Practical Course on Quantum Information Science and Quantum Computing

    A university-level course on Quantum Computing and Quantum Information Science that incorporates IBM Q Experience and Qiskit. This course is adequate for general audiences without prior knowledge on Quantum Mechanics and Quantum Computing (see prior knowledge), has an estimated average duration of 10 weeks at 3h/week (see duration), and is meant to be the entrypoint into the Quantum World.
    Downloads: 0 This Week
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