Hyperparameter Tuning in Microsoft Azure Machine Learning.pptx

hyperparameter tuning

1. Hyperparameter Tuning An Overview of Hyperparameter Optimization Techniques Presented by: Shreya Das Utsav S

2. Introduction to Hyperparameter Tuning Hyperparameters are parameters that are not learned from the data but are set prior to the training process. Effective tuning of these parameters can significantly improve the performance of machine learning models.

3. Why Tuning Hyperparameters is Crucial Optimizes model performance Prevents overfitting and underfitting Enhances the generalization ability of the model Improves prediction accuracy

4. Common Hyperparameter Tuning Methods Grid Search Random Search Bayesian Optimization Genetic Algorithms

5. Microsoft Azure Machine Learning Microsoft Azure Machine Learning is a cloud-based service for building, training, and deploying machine learning models. It provides robust tools for hyperparameter tuning, including automated and manual options.

6. Hyperparameter Tuning in Azure ML Azure ML offers multiple ways to tune hyperparameters: 1.Hyperparameter Tuning with Azure Machine Learning Experiments 2.HyperDrive for automated hyperparameter optimization 3.Custom scripts for manual tuning

7. Using HyperDrive for Tuning HyperDrive is a built-in feature of Azure ML that automates hyperparameter tuning. It supports multiple tuning strategies, including grid search and random search, and integrates seamlessly with Azure ML pipelines.

8. Setting Up HyperDrive Define a search space for hyperparameters. Choose a sampling method (e.g., grid or random). Specify a primary metric to optimize. Configure the number of iterations and early stopping criteria. Run HyperDrive and analyze results.

9. Best Practices for Hyperparameter Tuning Start with a smaller search space to save resources. Use cross-validation to ensure model robustness. Monitor performance metrics closely. Leverage parallel computing to speed up the process. Continuously refine hyperparameter ranges based on results.

10. Conclusion Effective hyperparameter tuning is essential for building high-performance machine learning models. Azure ML provides powerful tools and frameworks to facilitate this process, ensuring optimized model performance and efficient resource utilization.