Training a CNN for image classification

Once we have a good understanding of why and when to use DL models, we can start to actually implement one using Azure Machine Learning. We will start with a task that DL performed very well with over the past years, computer vision, or more precisely, image classification. If you feel that this is too easy for you, you can replace the actual training script with any other computer vision technique and follow along with the steps in this section:

• First, we will power up an Azure Machine Learning compute instance, which serves as our Jupyter Notebook authoring environment. We will first write a training script and execute it in the authoring environment to verify that it works properly, checkpoints the model, and logs the training and validation metrics. We will train the model for a few epochs to validate the setup, the code, and the resulting model.
• Once this is set up, we will try to improve the algorithm by adding data augmentation...