Hidden layers in neural network

I am extremely new to neural networks.

I would just like to ask if there is a need to have hidden layers in a neural network.

I read off Quora that a neural network with 10 input nodes and 10 output nodes will have 100 parameters and 10 bias units.

If thats the case it means there are no hidden layers?

• Every output node $j$ in your network first computes $z_{j}= \sum_{i=1}^{10}W_{j, i}x_{i} + b_{j}$ and then returns $\frac{1}{1 + exp(-z_{j})}$, where $x_{i}$ is input $i$. Now if you remove the $j$ index from this equation then this literally becomes the input-output mapping defined by logistic regression. Therefore when using a sigmoid activation function every output node of the network you describe is in fact a logistic regression unit (each with possibly different weights). See en.wikipedia.org/wiki/Logistic_regression#Basics "Definition of the Logistic Function" for this map – Sjoerd Nov 24 '15 at 16:12
• I ment en.wikipedia.org/wiki/… for the logistic mapping for a single variable $\beta$ – Sjoerd Nov 24 '15 at 16:18