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Build Deep Learning Applications Using PyTorch and Amazon SageMaker (AIM432-R1) - AWS re:Invent 2018

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In this workshop, learn how to get started with the PyTorch deep learning framework using Amazon SageMaker, a fully managed platform to build, train, and deploy machine learning (ML) models at scale quickly and easily. First, we create a computer vision model using deep neural networks that helps us discover analytical information from our image dataset. Then, we use Amazon Redshift, a fully managed data warehouse, to perform analytics and find business value using the output of our ML model.

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Build Deep Learning Applications Using PyTorch and Amazon SageMaker (AIM432-R1) - AWS re:Invent 2018

  1. 1. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Build Deep Learning Applications Using PyTorch and Amazon SageMaker Dylan Tong Solutions Architect Amazon Web Services A I M 4 3 2 Vikram Gangulavoipalyam Solutions Architect Amazon Web Services Yash Pant Solutions Architect Amazon Web Services
  2. 2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. PyTorch “An open source deep-learning platform that provides a seamless path from prototype to production.”
  3. 3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Key Features and Capabilities
  4. 4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Why PyTorch
  5. 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high performance algorithms One-click training Hyperparameter optimization BUILD TRAIN DEPLOY
  6. 6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Bring Your Own Script: PyTorch Container
  7. 7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  8. 8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  9. 9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Shared weights Similarity(v1,v2) Adam  min: contrastive loss(similarity(v1,v2),labels) v1 v2 CNN CNN ResNet-152 (transfer learning) Source: https://arxiv.org/abs/1311.2901 The CNN extracts features from images, which allows us to map images through training into a vector space that represents the similarity between images.
  10. 10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Advance analytics
  11. 11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Solution architecture
  12. 12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Related breakouts Monday, Nov 26 AIM402-R Deep Learning Applications Using PyTorch, Featuring Facebook 10:00 AM – 11:00 AM | Lando 4202 Tuesday, Nov 27 AIM402-R Deep Learning Applications Using PyTorch, Featuring Facebook 4:45 PM – 5:45 PM | Aria West, Level 3, Juniper 4
  13. 13. Thank you! © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  14. 14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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