Hyperparameter Tuning with Amazon SageMaker's Automatic Model Tuning - AWS Online Tech Talks
Learn how to use Automatic Model Tuning with Amazon SageMaker to get the best machine learning model for your dataset. Training machine models requires choosing seemingly arbitrary hyperparameters like learning rate and regularization to control the learning algorithm. Traditionally, finding the best values for the hyperparameters requires manual trial-and-error experimentation. Amazon SageMaker makes it easy to get the best possible outcomes for your machine learning models by providing an option to create hyperparameter tuning jobs. These jobs automatically search over ranges of hyperparameters to find the best values. Using sophisticated Bayesian optimization, a meta-model is built to accurately predict the quality of your trained model from the hyperparameters.
Learning Objectives:
- Understand what hyperparameters are and what they do for training machine learning models
- Learn how to use Automatic Model Tuning with Amazon SageMaker for creating hyperparameter tuning of your training jobs
- Strategies for choosing and iterating on tuning ranges of a hyperparameter tuning job with Amazon SageMaker
Видео Hyperparameter Tuning with Amazon SageMaker's Automatic Model Tuning - AWS Online Tech Talks канала AWS Online Tech Talks
Learning Objectives:
- Understand what hyperparameters are and what they do for training machine learning models
- Learn how to use Automatic Model Tuning with Amazon SageMaker for creating hyperparameter tuning of your training jobs
- Strategies for choosing and iterating on tuning ranges of a hyperparameter tuning job with Amazon SageMaker
Видео Hyperparameter Tuning with Amazon SageMaker's Automatic Model Tuning - AWS Online Tech Talks канала AWS Online Tech Talks
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
![Tune Your ML Models to the Highest Accuracy with Amazon SageMaker Automatic Model Tuning](https://i.ytimg.com/vi/xpZFNIOaQns/default.jpg)
![Thomas Huijskens - Bayesian optimisation with scikit-learn](https://i.ytimg.com/vi/jtRPxRnOXnk/default.jpg)
![Amazon SageMaker Studio Deep Dive - AWS Online Tech Talks](https://i.ytimg.com/vi/pGhn8Ax8QmQ/default.jpg)
![Build, Train, and Deploy Machine Learning Models at Scale Using AWS](https://i.ytimg.com/vi/iFWaMnyrTBY/default.jpg)
![AWS DeepRacer Local Training (deprecated)](https://i.ytimg.com/vi/CFNcKmtVRSI/default.jpg)
![Improve Data Science Team Productivity Using Amazon SageMaker Studio - AWS Online Tech Talks](https://i.ytimg.com/vi/-WzkbdioMJE/default.jpg)
![Explore/Exploit: Hyperparameter Tuning with W&B Sweeps by Stacey Svetlichnaya](https://i.ytimg.com/vi/6wRBGNLgQFU/default.jpg)
![AWS re:Invent 2019: Build accurate training datasets with Amazon SageMaker Ground Truth (AIM308)](https://i.ytimg.com/vi/6WJxzKsIFKA/default.jpg)
![Richard Liaw: A Guide to Modern Hyperparameters Turning Algorithms | PyData LA 2019](https://i.ytimg.com/vi/10uz5U3Gy6E/default.jpg)
![How to Train and Tune Your Models with Amazon SageMaker - AWS Online Tech Talks](https://i.ytimg.com/vi/Tnv6HsT1r4I/default.jpg)
![Dan Ryan: Efficient and Flexible Hyperparameter Optimization | PyData Miami 2019](https://i.ytimg.com/vi/IqQT8se9ofQ/default.jpg)
![](https://i.ytimg.com/vi/aTPC1xP3dMQ/default.jpg)
![Machine Learning Tutorial Python - 16: Hyper parameter Tuning (GridSearchCV)](https://i.ytimg.com/vi/HdlDYng8g9s/default.jpg)
![Gilles Louppe | Bayesian optimization with Scikit-Optimize](https://i.ytimg.com/vi/DGJTEBt0d-s/default.jpg)
![Deploying Machine Learning Models with mlflow and Amazon SageMaker](https://i.ytimg.com/vi/jpZSp9O8_ew/default.jpg)
![Hyperparameter Tuning with Katib](https://i.ytimg.com/vi/nIKVlosDvrc/default.jpg)
![Build, Train and Deploy Machine Learning Models on AWS with Amazon SageMaker - AWS Online Tech Talks](https://i.ytimg.com/vi/R0vC31OXt-g/default.jpg)
![Amazon SageMaker’s Built-in Algorithm Webinar Series: DeepAR Forecasting](https://i.ytimg.com/vi/g8UYGh0tlK0/default.jpg)
![How I Automated a Supply Chain with Machine Learning, AWS, and Python](https://i.ytimg.com/vi/x01N1kIQhUs/default.jpg)
![Machine Learning | Hyperparameter](https://i.ytimg.com/vi/cyIINCqyi5g/default.jpg)