That's a good point about longitude looping. I hadn't thought about that. I'm designing a classifier, and would like to include geographic location as one of the input variables.

Formatting lat/lng data for neural net feature input:

I've got latitude/longitude columns in a sql table that I'd like to add as features for a neural net classifier model. In terms of formatting for input, I plan to normalize latitude values to a range between 0-1, with 0 mapping to the largest possible negative lat value, and 1 mapping to the largest possible positive lat value. Then do the same for longitude, and pass them in as separate features.

Does that seem like a reasonable approach? Any other tricks I should know?

Discrete features as training data:

Say I am using SQL table rows as training data input for a deep neural net classifier. One of the columns contains a number from 1-5 representing a discrete value, say type of computer connection. It could be wifi, mobile-data, LAN, etc. What would be the best way to represent as input features? Right now I'm thinking split into a five dimensional vector, one for each possible value. Then pass 0 or 1 depending on whether a given feature is selected. I'm worried that including the range of values as a single vector would lead to messed up learning since one discrete value doesn't have any meaningful closeness to it's nearest discrete neighbor.