Length
1 day

Overview

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.

This intermediate-level course is delivered through a mix of instructor-led training (ILT), hands-on labs, and group exercises.

Please note: students are required to bring their own laptop/tablet and power cable for this course.

Key Topics

Detailed Info
  • Machine learning overview
  • Introduction to deep learning
  • Introduction to Apache MXNet
  • ML and DL architectures on AWS
Skills Gained
Key Topics
Target Audience
Prerequisites

Skills Gained

This course is designed to teach participants how to:

  • 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

Key Topics

Module 1: 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

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 multi-layer 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

Please note: This is an emerging technology course. Course outline is subject to change as needed.

Target Audience

This course is intended for:

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

Prerequisites

It is recommended that attendees have the following prerequisites:

  • Basic understanding of machine learning (ML) processes
  • Basic understanding of AWS core services like Amazon EC2 and knowledge of AWS SDK
  • Basic knowledge of a scripting language like Python

We can also deliver and customise this training course for larger groups – saving your organisation time, money and resources. For more information, please call us at +632 8244 2098 or email [email protected]

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The supply of this course by DDLS is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.

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Virtual Classroom
October 12 2021 - October 12 2021

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