书栈网 · BookStack 本次搜索耗时 0.027 秒,为您找到 2411 个相关结果.
  • Azure Machine Learning Components

    Azure Machine Learning Components Prerequisites Azure ML Register Model component Azure ML Deploy Model component Other Azure ML capabilities Azure Machine Learning Compone...
  • Robust Regression

    Robust Regression 1. 算法介绍 2. 分布式实现 on Angel 3. 运行 & 性能 输入格式 参数 性能 Robust Regression 鲁棒回归模型(robust regression model)同样是对一个或多个自变量与一个因变量之间的关系进行建模,不同点在于其旨在克服传统参数和非参数方法的一些局限性...
  • Margin

    Margin Default class reference Add margin to a single side Add horizontal margin Add vertical margin Add margin to all sides Negative margins Responsive Customizing Margin s...
  • Use Cases

    Use Cases Deploying and managing a complex ML system at scale Experimentation with training an ML model End to end hybrid and multi-cloud ML workloads Tuning the model hyperpara...
  • MNIST

    1125 2019-07-22 《MLeap Document》
    MNIST Demo Nouns Train a Spark Pipeline Load the data Build the ML Data Pipeline Train a Random Forest Model Serialize the ML Data Pipeline and RF Model to Bundle.ML Deseria...
  • 技术支持

    技术支持 技术支持 Xiaomi Cloud-ML研发团队:ml@xiaomi.com "">cloud-ml@xiaomi.com Xiaomi Cloud-ML用户支持:help@xiaomi.com "">cloud-ml-help@xiaomi.com 原文: http://docs.api.xiaomi.com/cloud-ml/fe...
  • Use Cases

    Use Cases Deploying and managing a complex ML system at scale Experimentation with training an ML model End to end hybrid and multi-cloud ML workloads Tuning the model hyperpara...
  • Margin

    Margin Default class reference Add margin to a single side Add horizontal margin Add vertical margin Add margin to all sides Negative margins Responsive Customizing Margin s...
  • Machine Learning

    1358 2020-01-13 《Dask 2.9.1 Document》
    Dask-ML What does this offer? How does this work? Parallelize Scikit-Learn Directly Reimplement Scalable Algorithms with Dask Array Partner with other distributed libraries Sc...
  • Use Cases

    Use Cases Deploying and managing a complex ML system at scale Experimentation with training an ML model End to end hybrid and multi-cloud ML workloads Tuning the model hyperpara...