Tutorial: End-to-end deep learning models on Azure Databricks

This tutorial notebook presents an end-to-end example of training a deep learning model in Azure Databricks, including loading data, visualizing the data, setting up a parallel hyperparameter optimization, and using MLflow to review the results, register the model, and perform inference on new data using the registered model in a Spark UDF.

The notebook uses PyTorch, a Python package that provides GPU-accelerated tensor computation and high level functionality for building deep learning networks.

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