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