I am a new student in the world of deep learning and after studying the functioning of logistic regression and neural networks there are some insights that probably escape me.
Given these two settings:
I have understood how the individual steps work, from forward prop to backward prop and optimisation via gardient descent, but these are steps that are taken in both cases, so my question is:
Intuitively what is the difference? In addition to the introduction of non-linearity because of the different activation functions, is there also any change in parameters? Efficiency ? Is it more accurate?