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  1. CITATION.cff

    management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling...
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  2. README.md

    deploy ML-powered applications.
    
    TensorFlow was originally developed by researchers and engineers working within
    the Machine Intelligence team at Google Brain to conduct research in machine
    learning and neural networks. However, the framework is versatile enough to be
    used in other areas as well.
    
    TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)
    and [C++](https://www.tensorflow.org/api_docs/cc) APIs, as well as a
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  3. ci/official/containers/linux_arm64/cuda.packages.txt

    # CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
    libcudnn8=8.9.6.50-1+cuda12.2
    libcudnn8-dev=8.9.6.50-1+cuda12.2
    
    # This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image.
    # For now it ensures that we install at least version 12.3.107 of PTXAS,
    # since 12.3.103 has a bug.
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  4. SECURITY.md

    or networks.
    
    For performance reasons, the default TensorFlow server does not include any
    authorization protocol and sends messages unencrypted. It accepts connections
    from anywhere, and executes the graphs it is sent without performing any checks.
    Therefore, if you run a `tf.train.Server` in your network, anybody with access
    to the network can execute arbitrary code with the privileges of the user
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  5. RELEASE.md

        install tensorflow` command.
    *   Several python API calls have been changed to resemble NumPy more closely.
    *   Android: person detection + tracking demo implementing Scalable Object
        Detection using Deep Neural Networks.
    *   New (experimental)
        [Java API](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/java).
    *   Add new Android image stylization demo based on "A Learned Representation
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