CourseInfo | SimpliTrain

Description

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.

Course 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.

Course Objective

This course is designed to teach you 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

Who Should Attend

This course is intended for: Developers 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

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Module 1: Machine learning overview Lesson Id 1

Module 1: Machine learning overview

Module 2: Introduction to deep learning Lesson Id 2

Module 2: Introduction to deep learning

Module 3: Introduction to Apache MXNet Lesson Id 3

Module 3: Introduction to Apache MXNet

Module 4: ML and DL architectures on AWS Lesson Id 4

Module 4: ML and DL architectures on AWS

Lesson Id	Title	Description
1	Module 1: Machine learning overview	A brief history of AI, ML, and DL<br />The business importance of ML<br />Common challenges in ML<br />Different types of ML problems and tasks<br />AI on AWS
2	Module 2: Introduction to deep learning	Introduction to DL <br />The DL concepts<br />A summary of how to train DL models on AWS<br />Introduction to Amazon SageMaker<br />Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model
3	Module 3: Introduction to Apache MXNet	The motivation for and benefits of using MXNet and Gluon<br />Important terms and APIs used in MXNet<br />Convolutional neural networks (CNN) architecture <br />Hands-on lab: Training a CNN on a CIFAR-10 dataset
4	Module 4: ML and DL architectures on AWS	AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk)<br />Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition)<br />Hands-on lab: Deploying a trained model for prediction on AWS Lambda

Deep Learning on AWS

Description

Course Overview

Course Objective

Who Should Attend