Showing 157 open source projects for "deep learning"

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  • 1
    Swift AI

    Swift AI

    The Swift machine learning library

    Swift AI is a high-performance deep learning library written entirely in Swift. We currently offer support for all Apple platforms, with Linux support coming soon. Swift AI includes a collection of common tools used for artificial intelligence and scientific applications. A flexible, fully-connected neural network with support for deep learning. Optimized specifically for Apple hardware, using advanced parallel processing techniques.
    Downloads: 0 This Week
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  • 2
    Grenade

    Grenade

    Deep Learning in Haskell

    Grenade is a composable, dependently typed, practical, and fast recurrent neural network library for concise and precise specifications of complex networks 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...
    Downloads: 0 This Week
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  • 3
    Face Verification Experiment

    Face Verification Experiment

    Original Caffe Version for LightCNN-9. Highly recommend to use PyTorch

    face_verification_experiment is a research repository focused on experiments in face verification using deep learning. It provides implementations and scripts for testing different neural network architectures and training strategies on face recognition and verification tasks. The project is designed to help researchers and practitioners evaluate the performance of models on standard datasets and explore techniques for improving accuracy.
    Downloads: 1 This Week
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  • 4
    PrettyTensor

    PrettyTensor

    Pretty Tensor: Fluent Networks in TensorFlow

    Pretty Tensor is a high-level API built on top of TensorFlow that simplifies the process of creating and managing deep learning models. It wraps TensorFlow tensors in a chainable object syntax, allowing developers to build multi-layer neural networks with concise and readable code. Pretty Tensor preserves full compatibility with TensorFlow’s core functionality while providing syntactic sugar for defining complex architectures such as convolutional and recurrent networks. ...
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  • 5
    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 in tensorflow, and much more. ...
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  • 6
    ConvNetJS

    ConvNetJS

    Deep learning in Javascript to train convolutional neural networks

    ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. ConvNetJS is an implementation of Neural networks, together with nice browser-based demos. It currently supports common Neural Network modules (fully connected layers, non-linearities), classification (SVM/Softmax) and Regression (L2) cost functions, ability to specify and train Convolutional Networks that process images, and experimental Reinforcement Learning modules, based on Deep Q Learning. ...
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  • 7

    CURRENNT

    CUDA-enabled machine learning library for recurrent neural networks

    CURRENNT is a machine learning library for Recurrent Neural Networks (RNNs) which uses NVIDIA graphics cards to accelerate the computations. The library implements uni- and bidirectional Long Short-Term Memory (LSTM) architectures and supports deep networks as well as very large data sets that do not fit into main memory.
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