A-9009.yaml

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speaker_name: "Heather Spetalnick"

talk_title: "Dog Classification using PyTorch"

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talk_tags:
- "python"
- "machine learning"
- "pytorch"
- "Microsoft Azure"
- "tutorial"
- "90 minutes"



talk_abstract: "Have you ever seen a dog and wanted to know what breed it was? In this tutorial, you will learn how to train an image classification model using 
  transfer learning."

talk_details: |

  The Azure Machine Learning SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for 
  both single-node and distributed runs on Azure compute. The model is trained to classify dog breeds first using a pretrained 
  ResNet model that has been trained on the [Stanford Dog dataset](https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fvision.stanford.edu%2Faditya86%2FImageNetDogs%2F&data=02%7C01%7Ca-pavuon%40microsoft.com%7Cc968724834db4a5e761408d63a9b9dd8%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C636760837538486038&sdata=eRna7uU4aoThNdRTmQafw90pXvYop%2FPNNvQO%2BAR6ae4%3D&reserved=0).
  
  To sign up for this tutorial, [follow  this link](https://github.com/pyconca/2018-wiki/wiki/Tutorials/)


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about_author: |

  Heather Spetalnick (formerly Shapiro) is a Program Manager from Boston on the Azure Machine Learning team. 
  She works to ensure successful user experiences across the Azure Machine Learning platform  for professional data scientists.
  Prior to becoming a Program Manager, Heather was a Technical Evangelist in New York, where she worked closely with partners,
  developer communities, and students to help them learn, adopt, and use preferred data science and machine learning practices 
  and tools from Microsoft. Heather completed her undergraduate degree at Duke University and graduated in the Class of 2015. 
  She received Bachelors of Science in Computer Science and Statistical Science, and completed an Honors Thesis about 
  employing Bayesian Approaches to Understanding Music Popularity. 
  Heather blogs at [http://microheather.com](http://microheather.com) and tweets at [http://twitter.com/microheather](http://twitter.com/microheather). 


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author_website: 'http://microheather.com'