# How does one make it obvious that the structure of a neural network should be what it is?

I am a beginner: I've only read a book about neural network and barely implemented one in C.

In short:

• A neural network is built out of nodes,
• Each node holds an output: activation.(sum.(x * w)),
• We then compute the total error out of the network output.

From a beginner perspective, hyper-parameters, such as the number of layers needed, seem to be defined arbitrarily in most tutorials, books. In fact, the whole structure seems to be quite arbitrarily defined. In practice, hyper-parameters are often defined based on some standards.

My question is, if you were to talk to a total beginner, how would you explain to him the structure of a neural network in such a way that the whole thing would appear as obvious ? Is that even possible ?

Here, the word structure refers to a neural network being a configuration of nodes inside layers.

Thanks to anyone pointing out ambiguities or spelling errors.

Edit: note that I actually understand the whole back-propagation algorithm. I have no problem visualizing a nn.

• Welcome to AI! Congrats on that first implementation. – DukeZhou May 1 '18 at 20:50
• Is your question asking about hyperparameters, or about "the structure of neural networks" in general – k.c. sayz 'k.c sayz' May 1 '18 at 22:51
• @k.c.sayz'k.csayz' my question is about the general structure (nodes interconnections) – user15357 May 2 '18 at 10:51