Deep Learning on AWS

Deep Learning on AWS is an intermediate-level instructor-led course covering AWS solutions for developing, training, and deploying deep learning workloads. The course introduces the relationship between artificial intelligence, machine learning, and deep learning, along with common deep learning concepts and use cases.

Participants learn to use Amazon SageMaker and the Apache MXNet framework for deep learning workloads, including neural-network model development and convolutional neural networks. The course also covers AWS deployment options such as AWS Lambda, AWS IoT Greengrass, Amazon ECS, and AWS Elastic Beanstalk, along with AWS AI services based on deep learning. The source curriculum includes hands-on activities for training models and deploying a trained model for prediction.

• Define machine learning and deep learning.
• Identify key concepts within the deep learning ecosystem.
• Explain the relationship between AI, machine learning, and deep learning.
• Use Amazon SageMaker for deep learning workloads.
• Apply the Apache MXNet programming framework and Gluon for deep learning.
• Understand convolutional neural network (CNN) architecture.
• Train a CNN using the CIFAR-10 dataset.
• Identify AWS services suitable for deploying deep learning models.
• Deploy a trained deep learning model for prediction using AWS Lambda.
• Identify AWS AI services based on deep learning, including Amazon Polly, Amazon Lex, and Amazon Rekognition.

• Basic understanding of machine learning processes.
• Knowledge of AWS core services such as Amazon EC2 and the AWS SDK.
• Basic knowledge of a scripting language such as Python.