Showing 4 open source projects for "mediathekview 10 mac"

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  • 1
    Malicious PDF Generator

    Malicious PDF Generator

    Generate a bunch of malicious pdf files with phone-home functionality

    Generate ten different malicious PDF files with phone-home functionality. Can be used with Burp Collaborator or Interact.sh. Used for penetration testing and/or red-teaming etc. I created this tool because I needed a third-party tool to generate a bunch of PDF files with various links.
    Downloads: 4 This Week
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  • 2
    PyG

    PyG

    Graph Neural Network Library for PyTorch

    PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support,...
    Downloads: 0 This Week
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  • 3
    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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  • 4
    Mixup-CIFAR10

    Mixup-CIFAR10

    mixup: Beyond Empirical Risk Minimization

    mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more...
    Downloads: 0 This Week
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