Deep learning on AWS

COURSE OUTLINE

Day 1  

Module 1: Machine learning overview

• A brief history of AL, ML, and DL
• The business importance of ML
• Common challenges in ML
• Different types of ML problems and task
• AI on Aws

Module 2: Introduction to deep learning 

• Introduction to DL
•  The DL concepts
•  A summary of how to train DL models on AWS 
• Introduction to Amazon SageMaker 
• Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multilayer perceptron neural network model

Module 3:  Introduction to Apache MXNet

• The motivation for and benefits of using MXNet and Gluon
•  Important terms and APIs used in MXNet 
•  Convolutional neural networks (CNN) architecture 
• Hands-on lab: Training a CNN on a CIFAR-10 dataset

Module 4: ML and DL architectures on AWS

•   AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk) 
• Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition) 
•  Hands-on lab: Deploying a trained model for prediction on AWS Lambda