Deep Learning on AWS
The Deep Learning on AWS Course is designed to provide participants with the knowledge and skills needed to use deep learning services provided by AWS. During the course, participants will learn how to use Amazon SageMaker to build, train, and deploy deep learning models. They will also be introduced to key deep learning algorithms and how to use them to solve supervised and unsupervised learning problems. The course is intended for software developers, data scientists, and other IT professionals interested in learning how to use AWS deep learning services. The course helps prepare for the AWS Certified Machine Learning – Specialty Certification .
Course Objectives
Below is a summary of the main objectives of the Deep Learning on AWS course :
- Gain knowledge and skills to use deep learning services provided by AWS.
- Learn how to use Amazon SageMaker to build, train, and deploy deep learning models.
- Be introduced to the main deep learning algorithms.
- Apply deep learning algorithms to solve supervised and unsupervised learning problems.
- Apply the skills you gain to software developers, data scientists, and other IT professionals.
- Hyperparameter Optimization: Learn techniques and best practices for optimizing hyperparameters in deep learning models using AWS SageMaker’s built-in capabilities.
- Model Deployment and Management: Gain skills in deploying trained deep learning models into production environments on AWS, and manage them effectively for ongoing performance and scalability.
- Integration with AWS AI Services: Explore integration possibilities with other AWS AI services such as Amazon Rekognition for image analysis and Amazon Comprehend for natural language processing, enhancing the functionality of deep learning applications
Course Certification
This course helps you prepare to take the:
AWS Certified Machine Learning – Specialty Exam ;
Course 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
- Reconnaissance)
- Hands-on lab: Deploying a trained model for prediction on AWS Lambda
Course Mode
Instructor-Led Remote Live Classroom Training;
Trainers
Trainers are Amazon AWS accredited instructors and certified in other IT technologies, with years of practical experience in the sector and in training.
Lab Topology
For all types of delivery, the participant can access the equipment and actual systems in our laboratories or directly in international data centers remotely, 24/7. Each participant has access to implement various configurations, Thus immediately applying the theory learned. Below are some scenarios drawn from laboratory activities.
Course Details
Course Prerequisites
- Attendance at the AWS Technical Essentials Course and the Python Developer Course is recommended .
Course Duration
Intensive duration 1 days;
Course Frequency
Course Duration: 1 days (9.00 to 17.00) - Ask for other types of attendance.
Course Date
- Deep Learning on AWS(Formula Intensiva) – On Request – 9:00 – 17:00
Steps to Enroll
Registration takes place by asking to be contacted from the following link, or by contacting the office at the international number +355 45 301 313 or by sending a request to the email info@hadartraining.com
