I'm working on a `Regression`

problem where I'm going to use `Neural Networks`

for predictions. I have already preprocessed the input dataset.

After the preprocessing I have approximately the following (listing just the key points):

- 10 continuous variables
- 20 discrete variables which are converted to numeric with
`one hot encoding`

After the `one hot encoding`

I have:

- 300 numeric variables / columns (the
`one hot encoding`

created a new column for each`discrete`

value)

I know that when training `Neural Networks`

you have to try multiple combinatioins, but I want to open a debate about this.

**My Question:** Based on the key points above, what would be a good starting point for the hidden layer structure of the `Neural Networks`

?

Thanks!