The detailed documentation for this image classification example includes the step-by-step walk-through: https://docs.microsoft.com/azure/machine-learning/preview/scenario-image-classification-using-cntk

The public GitHub repository for this image classification example contains all the code samples: https://github.com/azure/MachineLearningSamples-ImageClassificationUsingCntk

A large number of problems in the computer vision domain can be solved using image classification approaches. These include building models which answer questions such as, "Is an OBJECT present in the image?" (where OBJECT could for example be "dog", "car", "ship", etc.) as well as more complex questions, like "What class of eye disease severity is evinced by this patient's retinal scan?"

This tutorial will address solving such problems. We will show how to train, evaluate and deploy your own image classification model using the Microsoft Cognitive Toolkit (CNTK) for deep learning. Example images are provided, but the reader can also bring their own dataset and train their own custom models.

The key steps required to deliver this solution are as follows:

1. Generate an annotated image dataset. Alternatively, the provided demo dataset can be used.
2. Train an image classifier using a pre-trained Deep Neural Network.
3. Evaluate and improve accuracy of this model.
4. Deploy the model as a REST API, either to the local machine or to the cloud.

1. An Azure account (free trials are available).
2. An installed copy of Azure Machine Learning Workbench with a workspace created.
3. A machine or VM running Windows.
4. A dedicated GPU is recommended, however not required.

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