Deep Learning on AWS

In this course, you’ll learn about AWS’s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You’ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You’ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS.

Duration: 1 Day

Prerequisites

• Basic understanding of ML processes
  
• Basic understanding of AWS core services like Amazon EC2
  
• Knowledge of AWS SDK
  
• Basic understanding of a scripting language like Python

Audience

• Developers who are responsible for developing deep learning applications
  
• Developers who want to understand the concepts behind deep learning and how to implement a deep learning solution on AWS Cloud

Learning Objectives

• Define machine learning (ML) and deep learning
  
• Identify the concepts in a deep learning ecosystem
  
• Use Amazon SageMaker and the MXNet programming framework for deep learning workloads
  
• Fit AWS solutions for deep learning deployments

Topics

• Machine Learning Overview
  
• Introduction to Deep Learning
  
• Introduction to Amazon SageMaker
  
• Introduction to Apache MXNet
  
• Benefits of using MXNet and Gluon
  
• Convolutional Neural Networks (CNN) Architecture
  
• AWS Services for Deploying DL Models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk)
  
• Introduction to AWS AI Services Based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition)
  
• Deploying Trained Model for Prediction on AWS Lambda