I'm newbie in machine learning and trying to understand neural network. I know how logistic regression works and on this basis I try to understand how the neural network works. I'm trying to grasp intuitively how NN works. Could you check, please, if I understand correctly:
- neural network it's roughly saying just extension of logistic regression (in case when we solve classification problem and use sigmoid function);
- logistic regression trying to aproximate data with sigmoid curve;
- neural network it's a set of functions which approximate input data (in case with one hidden layer. If NN have more than one hiidden layer i just can't understand how to extend it)
Also I have question: If I understood correctly that NN is a set of functions which approximate input data, why output layer is needed (or more layers)? For example, we have one hidden layer with 4 nodes and output layer with one node. I suppose that we've got 4 functions, which approximates input data in different ways and than output layer approximates output of hidden layer. I don't know how to think about it in context of approximation.