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  • 1.5. Stochastic Gradient Descent

    1.5. Stochastic Gradient Descent 1.5.1. Classification 1.5.2. Regression 1.5.3. Stochastic Gradient Descent for sparse data 1.5.4. Complexity 1.5.5. Stopping criterion 1.5.6. ...
  • Sedna

    Sedna What is Sedna? Features Architecture Sedna’s edge-cloud synergy is implemented based on the following capabilities provided by KubeEdge: Component GlobalManager LocalCon...
  • Sedna

    Sedna What is Sedna? Features Architecture Sedna’s edge-cloud synergy is implemented based on the following capabilities provided by KubeEdge: Component GlobalManager LocalCon...
  • Sedna

    Sedna What is Sedna? Features Architecture Sedna’s edge-cloud synergy is implemented based on the following capabilities provided by KubeEdge: Component GlobalManager LocalCon...
  • Sedna

    Sedna What is Sedna? Features Architecture Sedna’s edge-cloud synergy is implemented based on the following capabilities provided by KubeEdge: Component GlobalManager LocalCon...
  • Sedna

    Sedna What is Sedna? Features Architecture Sedna’s edge-cloud synergy is implemented based on the following capabilities provided by KubeEdge: Component GlobalManager LocalCon...
  • Trainer

    HFTrainer Example Training tasks PEFT Methods __call__(base, train, validation=None, columns=None, maxlength=None, stride=128, task='text-classification', prefix=None, metrics=N...
  • Machine Learning Overview

    Machine Learning Overview The Problem Setting Loading Dataset Learning Predicting Machine Learning Overview From this article, we will introduce what is machine learning an...
  • Quick Start in 3 Minutes

    Quick Start in 3 Minutes Example Code Explanation FAQ Quick Start in 3 Minutes This article introduces how to quickly get start with OneFlow. We can complete a full neural ne...
  • Components

    Components Central Dashboard Notebook Servers Kubeflow Pipelines KFServing Katib Training Operators Multi-Tenancy Components Logical components that make up Kubeflow ...