Showing 4 open source projects for "visual\"

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    Tiny CUDA Neural Networks

    Tiny CUDA Neural Networks

    Lightning fast C++/CUDA neural network framework

    This is a small, self-contained framework for training and querying neural networks. Most notably, it contains a lightning-fast "fully fused" multi-layer perceptron (technical paper), a versatile multiresolution hash encoding (technical paper), as well as support for various other input encodings, losses, and optimizers. We provide a sample application where an image function (x,y) -> (R,G,B) is learned. The fully fused MLP component of this framework requires a very large amount of shared...
    Downloads: 2 This Week
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    Accord.NET Framework

    Accord.NET Framework

    Scientific computing, machine learning and computer vision for .NET

    The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono.
    Downloads: 0 This Week
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  • 3
    Awesome Recurrent Neural Networks

    Awesome Recurrent Neural Networks

    A curated list of resources dedicated to RNN

    A curated list of resources dedicated to recurrent neural networks (closely related to deep learning). Provides a wide range of works and resources such as a Recurrent Neural Network Tutorial, a Sequence-to-Sequence Model Tutorial, Tutorials by nlintz, Notebook examples by aymericdamien, Scikit Flow (skflow) - Simplified Scikit-learn like Interface for TensorFlow, Keras (Tensorflow / Theano)-based modular deep learning library similar to Torch, char-rnn-tensorflow by sherjilozair, char-rnn...
    Downloads: 0 This Week
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  • 4
    DeepDream

    DeepDream

    This repository contains IPython Notebook with sample code

    DeepDream is a small, educational repository that accompanies Google’s original “Inceptionism” blog post by providing a runnable IPython/Jupyter notebook that demonstrates how to “dream” through a convolutional neural network. The notebook shows how to take a trained vision model and iteratively amplify patterns the network detects, producing the hallmark surreal, hallucinatory visuals. It walks through loading a pretrained network, selecting layers and channels to maximize, computing...
    Downloads: 0 This Week
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