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Building a Pet Detector in 30 minutes or less!

Play Building a Pet Detector in 30 minutes or less!
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Ever wondered what breed that dog or cat is? In this show, you'll see us train, optimize and deploy a deep learning model using Azure Notebooks, Azure Machine Learning Service, and Visual Studio Code using Python. We use transfer learning to retrain a mobilenet model using Tensorflow to recognize dog and cat breeds using the Oxford IIIT Pet Dataset. Next, we'll optimize that model using the Azure Machine Learning Service HyperDrive service, and improve the accuracy of our model to over 90%. Finally, we'll put on our developer hat, and use Visual Studio Code and our Python Extension to deploy and test our model. Along the way you'll see cool features like our new Jupyter-powered interactive programming experience in VS Code, our AI powered IntelliSense feature called Intellicode, and our Azure Machine Learning extension.


Github repo for all code used in the show:

Blog post introducing the new features in Azure Notebooks:

Blog post introducing our data science features in our Python extension:

Azure Notebooks:

Python Extension:

Azure Machine Learning Extension:

Visual Studio Code:





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