Deep neural net modelling strategy are there any resources out there (book, blog, your own answer post etc.) that gives advise on modelling strategy of deep neural net?

I know how to fit a neural net, I know how to change settings like number of hidden layers, activation function, dropout etc. I know how to use cross-validation to validate models.

But what I need is advise on the actual modelling process. i.e. given a dataset (which has been cleaned and explored), where do you start? What type of neural net do you train first? How do you then tune it?
I cannot recommend highly enough this online book on neural networks.

The tricky part about neural networks is the stuff you mentioned -- what value to use for the learning rate, what topology to use for the network, etc etc -- we call these things hyperparameters to distinguish from parameters which are estimated by the optimization process.

This big section of the aforementioned book covers exactly that. I don't know if you'll find what you want though. The reason is because there is no silver bullet.