Course Number: AWS-156

Duration: 1 day (6.5 hours)

Format: Live, hands-on

AWS Deep Learning Training Overview

This on-site or online Deep Learning on AWS training course teaches attendees how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. Students also learn to deploy their deep learning models using services like AWS Lambda while designing intelligent systems on AWS.

Accelebrate is an AWS Training Partner (ATP) and this hands-on official AWS Classroom Training course is taught by an accredited Amazon Authorized Instructor (AAI).

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some Cloud courses are available as live, online classes for individuals.

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

Prerequisites

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

Outline

Expand All | Collapse All

Machine learning Overview
  • A brief history of AI, ML, and DL
  • The business importance of ML
  • Common challenges in ML
  • Different types of ML problems and tasks
  • AI on AWS
Introduction to Deep Learning
  • Introduction to DL
  • The DL concepts
  • A summary of how to train DL models on AWS
  • Introduction to Amazon SageMaker
  • Spinning up an Amazon SageMaker notebook instance and running a multilayer perceptron neural network model
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
  • Training a CNN on a CIFAR-10 dataset
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)
  • Deploying a trained model for prediction on AWS Lambda

Training Materials

All AWS training students receive comprehensive courseware.

Software Requirements

A modern web browser and an Internet connection that allows connections by SSH or Remote Desktop (RDP) into AWS virtual machines.



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