A tanfolyamról
This 8 hours Deep Learning on AWS course is designed to help you get acquainted with advanced Machine Learning and Deep Learning concepts. By the end of the course, you will learn how to deploy Deep Learning on AWS. This is an advanced level training course that helps you to develop advanced Machine Learning and Deep Learning skills to train Deep Learning models. Also, you get to use state-of-the-art AWS services like AWS Lambda for deployment of Deep Learning solutions on AWS.
By the end of this course, you will learn to:
- Form a solid Machine Learning base
- Form a solid Deep Learning base
- Use Amazon SageMaker for running multi-layer perceptions
- Set up Deep Learning AMI
- Deploy Deep Learning models with AWS Lambda
Who Should Attend this Course
- Developers accustomed to Deep Learning applications
- Developers looking to implement Deep Learning solution on AWS
What You Will Learn
-
Machine Learning Concepts
Understand the importance of ML, discover common challenges in ML, and the latest ML frameworks -
Deep Learning Concepts
Get a complete understanding of DL concepts and learn to train DL models on the AWS platform -
Leverage Amazon SageMaker
Learn to use Amazon SageMaker for running a multi-layer perception on a neural network model -
Setting Up Deep Learning AMI
Running multi-layered perception neuron model with the help of a Deep Learning AMI instance -
Use Apache MXNet
Understand the benefits of MXNet and learn about convolutional neural networks (CNN) architecture -
Deploying Deep Learning
Learn to use AWS services like AWS Lambda for deploying a fully scalable Deep Learning model on Cloud
We provide this course in English.
Tematika
Curriculum
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
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
3. Introduction to Apache MXNet Topics:
- 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
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
Kinek ajánljuk
Előfeltételek
Prerequisites
- Basic understanding of ML processes
- Basic understanding of AWS core services
- Basic understanding of a scripting language like Python