Using categorical and continuous variables in Deep Learning
If I understand well your main issue is to transform some Categorical variables into Continuous, or some Continuous into Categorical.
You have many ways to do that:
Continuous to Categorical :

You can yourself set intervals, transforming your continuous value into a category. For example, for a variable Age, you define the following intervals : [0;10[, [10;20[, ... [90;100[. If someone is 36 yo, he'll be on the interval [30;40[. You'll then only have 10 categories. The complicated part is to find what are the best intervals to lose avoid losing to much information

Categorical to Continuous

You can apply some Sklearn Category Encoder, which, knowing your targer on training set, will transform each category to a value between 0 and 1. This value is calculated using statistics (different for each encoder), and illustrates how often your category ends up affecting your target.