If you’re here to teach students how to build machine learning models and deploy it to AWS DeepLens or if you’re looking for ideas on how to incorporate DeepLens into your curriculumn, you’ve come to the right section.
We will use some machine learning terminology in this section, if you’re not familiar with machine learning terminology, refer to the terminology section.
Our tutorials are organized by level of difficulty. If you or your students are completely new to machine learning and AWS, start at the beginner section.
In the beginner section, we will familiarize students with the DeepLens camera and its features. Students will deploy sample machine learning projects that are pre-built and see how they perform. Students are not expected to have any coding skills to complete this section. This is a great section for demos of what machine learning can do have have the students visualize how computers see the world.
In the intermediate section, students will learn how to combine many AWS services to build out applications that uses the power of Amazon Rekognition, a machine learning service for image and video analysis with powerful cloud-based models built by AWS. Students are expected to have basic coding skills to complete this section.
Finally in the advanced section, students will dive deep into machine learning and build their own custom machine learning models and deploy them to AWS DeepLens. Students are expected to have basic coding skills to complete this section.
To start you will need an AWS account. If you don’t have one already, create an AWS account for yourself here.
In the next section, we’ll walk through how to give your students access to AWS.