Machine learning (ML) and deep learning (DL) projects are increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics.
Wenming Ye and Miro Enev offer an overview of deep learning along with hands-on Jupyter labs, demos, and instruction. They begin by exploring the trends powering AI/DL adoption and the basics of algorithmic learning in neural networks. Wenming and Miro then dive into how DL is applied in modern business practice and demonstrate how to leverage building blocks from the Amazon ML family of AI services.
Trends and motivation for deep learning (DL)
Basics of neural learning and common DL architectures (CNN and AE)
Understanding a DL project workflow
Introduction to Amazon ML services:
SageMaker, Rekognition (Image and Video), Lex, Comprehend, Translate, Transcribe, and Polly
Hands-on exercise: Build and run your first deep learning model on an Amazon EC2 GPU instance with the Jupyter Notebook
Hands-on exercise: Introduction to deep learning and SageMaker (a Jupyter-based service)
Hands-on exercise: Anomaly detection using auto-encoders
Demo: Greengrass ML inference edge models
Hands-on exercise: Introduction to visual search
Architecture group discussion (edge-to-cloud AI, retail, etc.)
Hands-on exercise: Time series prediction using recurrent neural networks
Introduction to reinforcement learning
Wenming Ye is a senior solution architect at Amazon Web Services.
Miro Enev is a senior solutions architect at NVIDIA, where he helps train and guide pilot deep learning projects at Amazon. Miro’s interests include advancing data science and machine intelligence while respecting human values in future technology ecosystems.
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