5 projects for "deep learning toolbox" with 2 filters applied:

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  • 1
    tinygrad

    tinygrad

    Deep learning framework

    This may not be the best deep learning framework, but it is a deep learning framework. Due to its extreme simplicity, it aims to be the easiest framework to add new accelerators to, with support for both inference and training. If XLA is CISC, tinygrad is RISC.
    Downloads: 1 This Week
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  • 2
    MLJ

    MLJ

    A Julia machine learning framework

    MLJ (Machine Learning in Julia) is a toolbox written in Julia providing a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing about 200 machine learning models written in Julia and other languages. The functionality of MLJ is distributed over several repositories illustrated in the dependency chart below. These repositories live at the JuliaAI umbrella organization.
    Downloads: 1 This Week
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  • 3
    pipeless

    pipeless

    A computer vision framework to create and deploy apps in minutes

    Pipeless is an open-source computer vision framework to create and deploy applications without the complexity of building and maintaining multimedia pipelines. It ships everything you need to create and deploy efficient computer vision applications that work in real-time in just minutes. Pipeless is inspired by modern serverless technologies. It provides the development experience of serverless frameworks applied to computer vision. You provide some functions that are executed for new...
    Downloads: 0 This Week
    Last Update:
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  • 4
    KotlinDL

    KotlinDL

    High-level Deep Learning Framework written in Kotlin

    ...This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning models in production environments.
    Downloads: 0 This Week
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  • 5
    Mocha.jl

    Mocha.jl

    Deep Learning framework for Julia

    Mocha.jl is a deep learning framework for Julia, inspired by the C++ Caffe framework. It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia.
    Downloads: 0 This Week
    Last Update:
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