AWS-DL

New Deep Learning on AWS

Form of participation
Form of training
Length of training
  • 1 day (1×8 Lessons)
  • daily 9:00 - 17:00
Available languages
  • Hungarian
Dates

Training price

277 000 Ft
+ VAT/person
Please choose the date and form of participation!
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Description

In this course, we will learn about AWS Deep Learning (DL) solutions, with a focus on scenarios where DL can be of real use, and the principles of how Deep Learning works. We will learn how to run DL models in the cloud using Amazon SageMaker and the MXNet framework. We will also learn how to deploy DL models using services like AWS Lambda while designing intelligent systems on AWS.

The course consists of lectures, group exercises and lab exercises.

What will you learn in the course?

  • The differences between Machine Learning and Deep Learning.
  • How to define goals in the DL ecosystem.
  • How to use the Amazon SageMaker and MXNet frameworks for DL work
  • How to integrate AWS solutions with DL deployments

Suggested For

  • For developers who are working on Deep Learning applications.
  • Developers who want to understand the main DL guidelines and how they are applied in the AWS cloud.

    • Outline

    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 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
    Outline (PDF)

    Prerequisites

    We recommend the following to our participants:

    • Basic knowledge of ML processes
    • Knowledge of AWS core services (e.g. Amazon EC2 and AWS SDK)
    • Knowledge of Python or other programming language

    After Course

    After completing this course, we offer Practical Data Science with Amazon SageMaker and Amazon SageMaker Studio for Data Scientists .