# Understanding Feed Forward Neural Network

My problem with FFNN is that I do not understand in which use cases this network makes sense. Does anyone have an example where this is used? Once I read on the internet that it could be used for YES/NO Questions. For example the inputs are attributes of a human and after going through all the hidden nodes there will be an answer like 1 or 0 if that human is married or some sort of stuff. My Question now is: How do you define those hidden nodes. I mean every Innput Node is connected to every hidden node in the first layer. Why do I need more than one hidden node. I think I just need a good example where I understand how to define hidden nodes.

• So, what is your question: 1. when to use FFNNs or 2. how to determine the number of hidden nodes? – nbro Apr 8 at 10:08
• More like when to use it to understand how to determine the number of hidden nodes :D – Justin Holze Apr 8 at 11:48

## 1 Answer

We can add multiple hidden layers which contain neurons in a Feed Forward neural network.

For a neuron in the hidden layer,

$$output = act( w . x + b )$$

Where $$act$$ is the activation function.

Here, the value of $$x$$ could be the input features ( if the neuron lies in the first hidden layer ) or it could be the activations from the previous layer ( if the neuron lies in an intermediate hidden layer ).

With some more comfort, we can write the above equation as,

$$output = act(\sum_{i=0}^{N} w_i x_1 + b )$$