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  • 1
    dlib

    dlib

    Toolkit for making machine learning and data analysis applications

    ...Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is about 1 to 4. The library is tested regularly on MS Windows, Linux, and Mac OS X systems. No other packages are required to use the library, only APIs that are provided by an out of the box OS are needed. There is no installation or configure step needed before you can use the library. All operating system specific code is isolated inside the OS abstraction layers which are kept as small as possible.
    Downloads: 11 This Week
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  • 2
    MegEngine

    MegEngine

    Easy-to-use deep learning framework with 3 key features

    MegEngine is a fast, scalable and easy-to-use deep learning framework with 3 key features. You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR algorithm. Gain the lowest memory usage when inferencing a model by leveraging our unique pushdown memory planner. ...
    Downloads: 11 This Week
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  • 3
    Make-A-Video - Pytorch (wip)

    Make-A-Video - Pytorch (wip)

    Implementation of Make-A-Video, new SOTA text to video generator

    Implementation of Make-A-Video, new SOTA text to video generator from Meta AI, in Pytorch. They combine pseudo-3d convolutions (axial convolutions) and temporal attention and show much better temporal fusion. The pseudo-3d convolutions isn't a new concept. It has been explored before in other contexts, say for protein contact prediction as "dimensional hybrid residual networks". The gist of the paper comes down to, take a SOTA text-to-image model (here they use DALL-E2, but the same learning...
    Downloads: 5 This Week
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  • 4
    BerryNet

    BerryNet

    Deep learning gateway on Raspberry Pi and other edge devices

    ...Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events shown in the images or videos without connecting to the cloud. One of the applications of this intelligent gateway is to use the camera to monitor the place you care about. For example, Figure 3 shows the analyzed results from the camera hosted in the DT42 office. ...
    Downloads: 0 This Week
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  • 5
    Tensor2Tensor

    Tensor2Tensor

    Library of deep learning models and datasets

    Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and compare the results. ...
    Downloads: 1 This Week
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  • 6
    Deep Learning with Keras and Tensorflow

    Deep Learning with Keras and Tensorflow

    Introduction to Deep Neural Networks with Keras and Tensorflow

    ...The easiest way to get (most) these is to use an all-in-one installer such as Anaconda from Continuum. These are available for multiple architectures.
    Downloads: 0 This Week
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  • 7
    Grenade

    Grenade

    Deep Learning in Haskell

    ...Because the types are so rich, there's no specific term level code required to construct this network; although it is of course possible and easy to construct and deconstruct the networks and layers explicitly oneself. Networks in Grenade can be thought of as a heterogeneous list of layers, where their type includes not only the layers of the network but also the shapes of data that are passed between the layers. To perform back propagation, one can call the eponymous function which takes a network, appropriate input, and target data, and returns the back propagated gradients for the network. The shapes of the gradients are appropriate for each layer and may be trivial for layers like Relu which have no learnable parameters.
    Downloads: 0 This Week
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