Please explain like I'm a 5 years old.

Maybe I understand the following :
(my apologies if this is grossly simplified and doesn't help)

1- Better neural network need to contain more (stacked) layers.
2- input layer at one end of the stack is exposed to messy informations from the real world.
3- at the other end the output layer provide results from the network.
4- the first step for making this work is the training of the network during which training, learning is done.
5- instabilities and stagnation in some layers often occur when learning does not occur in an optimal way. This problem increases exponentially with the number of layers.
6- here learning is done all at once to all the layers. Something called rotation which I don't understand, is used to stabilize and optimize the learning.

I feel this is very different from human learning where it happens in stages : we first learn words, then try to assemble them to form simple sentences, then evolve to make sense of more complex notions and so on. I wish this approach could apply also in artificial intelligence development.