How to model categorical variables / enums? I am new to the field and I am trying to understand how is possible to use categorical variables / enums?

Lets say we have a data set and 2 of its features are home_team and away_team, the possible values of these 2 features are all the NBA teams.
How can we "normalize" these features to be able to use them to create a deep network model (e.g. with tensorflow)?

Any reference to read about techniques of modeling that are also very appreciated.
Authors use many different approaches.

One approach is to have a different input neuron for each possible category, and then use a "1-hot" encoding. So if you have 10 categories, then you can encode this as 10 binary features.

Another is to use some sort of binary encoding. If you have 10 categories, it is sufficient to use 4 neurons to represent all possible categories by using binary numbers.

A third approach is to convert your categories to cardinal values, and then normalize them. This may be more effective if your categories really are cardinal (i.e. orderable). If there isn't a natural ordering to them though, this might lead to strange results or make the problem difficult to learn (since it ends up embedding non-linear relationships in the learning problem that don't need to exist).