Put deep learning to work: A practical introduction using Amazon Web Services
9:00am - 5:00pm
What you'll learn, and how you can apply it
- Understand deep learning, TensorFlow, PyTorch, MXNet, and key trends and business scenarios in AI/DL adoption
- Learn to bring your models to production faster, with much less effort, and lower cost
Who is this presentation for?
- You're an aspiring ML developer, practicing ML developer, or data scientist.
A working knowledge of Python
You’ll explore the current trends powering AI/DL adoption and algorithmic learning in neural networks, dive into how DL is applied in modern business practices, and leverage building blocks from the Amazon ML family of AI services from powerful new GPU instances, convenient Amazon SageMaker built-in algorithms to ready-to-use managed AI services.
- Deep learning and reinforcement learning trends
- Neural learning and common DL architectures
- Understanding a DL project workflow by example
- Introduction to high-level Amazon ML Services
- Amazon SageMaker (a Jupyter-based service) with Amazon Elastic Inference
- Running Tensorflow and PyTorch on SageMaker
- Group discussion: Bring your own deep learning problem
- SageMaker custom and built-in algorithms
- Time series prediction using recurrent neural networks
- Current topics in NLP
- Introduction to reinforcement learning and the AWS DeepRacer
About your instructors
Wenming Ye is an AI/ML solutions architect at Amazon Web Services, helping researchers and enterprise customers to use cloud-based machine learning services to rapidly scale their innovations. Previously, Wenming had a diverse R&D experience at Microsoft Research, SQL engineering team, and successful startups.
Miro Enev is a senior solutions architect at NVIDIA, specializing in advancing data science and machine intelligence while respecting human values. He supports the Pacific Northwest teams engaged with cloud, industrial, and retail clients while participating in research in deep reinforcement learning and edge-to-cloud AI. Miro holds a PhD from the University of Washington’s computer science and engineering department, where his thesis was on machine learning applications for information privacy in emerging sensor contexts. He studied cognitive science and computer science as an undergraduate at the University of California, Berkeley.
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