The difference between these two types of can be described by the following analogy:

• Discriminative modeling: Observe paintings and determine the style of painting based on observations.

Here are a few steps that describe how we would do this in machine learning:

1. First, we create a machine learning model that use convolutional layers or other learned features to understand the divisions in the data
2. Next, we collect a dataset that has both a training set (60-90% of your data) and a validation dataset (10-40% of your data)
3. Train the machine learning model using your data

4.  Use this model to predict which datapoint belongs to a particular class - in our example, which painting belongs to which author

• Generative modeling: Learn and reproduce paintings in various painters' styles and determine the painting style from the styles you learned.

Here are a few steps to describe a possible way to accomplish this type of modeling:

1. Create a machine learning model that learns how to reproduce different painting styles
2. Collect a training and validation dataset
3. Train the machine learning model using the data
4. Use this model to predict (inference) to produce examples of the paint author - use similarity metrics to verify the ability of the model to reproduce the painting style.