Remove 2012 Remove Auto-classification Remove Auto-complete
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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml Launch SageMaker Studio Complete the following steps to launch your SageMaker Studio domain: On the SageMaker console, choose Domains in the navigation pane.

ML 123
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Grading Complex Interactive Coding Programs with Reinforcement Learning

The Stanford AI Lab Blog

It is well known that grading is critical to student learning 2 , in part because it motivates students to complete their assignments. Figure 7 : Performance of different bug classification models with different RL agents. For example, variational auto-encoder started only with 32% precision, but it increased to 74.8%