Showing 4 open source projects for "contract bridge on linux"

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    Custom VMs From 1 to 96 vCPUs With 99.95% Uptime

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    DiffEqFlux.jl

    DiffEqFlux.jl

    Pre-built implicit layer architectures with O(1) backprop, GPUs

    DiffEqFlux.jl is a Julia library that combines differential equations with neural networks, enabling the creation of neural differential equations (neural ODEs), universal differential equations, and physics-informed learning models. It serves as a bridge between the DifferentialEquations.jl and Flux.jl libraries, allowing for end-to-end differentiable simulations and model training in scientific machine learning. DiffEqFlux.jl is widely used for modeling dynamical systems with learnable...
    Downloads: 0 This Week
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  • 2
    OpenMLSys-ZH

    OpenMLSys-ZH

    Machine Learning Systems: Design and Implementation

    This repository is the Chinese translation (or localization) of the OpenMLSys project documentation. Its aim is to make the technical content, tutorials, architecture descriptions, and user guides of the OpenMLSys system more accessible to Chinese-speaking users. The repo mirrors the structure of the original OpenMLSys docs: sections on system design, API references, deployment instructions, module overviews, and example workflows. It helps bridge language barriers in open machine learning...
    Downloads: 0 This Week
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  • 3
    YOLOX

    YOLOX

    YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5

    YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. Prepare your own dataset with images and labels first. For labeling images, you can use tools like Labelme or CVAT. One more thing worth noting is that you should also implement pull_item and load_anno method...
    Downloads: 12 This Week
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  • 4
    Machine-Learning

    Machine-Learning

    kNN, decision tree, Bayesian, logistic regression, SVM

    Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying...
    Downloads: 0 This Week
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