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 course teaches you how to:
Define machine learning and deep learning
Identify the concepts in a deep learning ecosystem
Leverage MXNet programming framework for deep learning workload
Fit AWS solutions for deep learning deployments
Zielgruppen
Developers who are responsible for developing deep learning applications
Developers who want to understand the concepts behind deep learning and how to implement a deep learning solution on AWS Cloud
Vorkenntnisse
ML processes
AWS core services like Amazon EC2 and knowledge of AWS SDK
A scripting language like Python
Detail-Inhalte
Day One 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
Trainings Übersicht
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