FEED FORWARD NEURAL NETWORK

 Shagufta
5th Sem BCA

 

The Feed Forward neural network was the first and simplest type of artificial neural network devised. The network is called feed forward because it is forward in nature. The information travels forward in the network without any loops, first through the input nodes, then through the hidden nodes (if present), and finally through the output nodes. The series of hidden layers defines the accuracy of the generated output. Here the connections between units do not form a circle.

Feed forward neural network are primarily used for supervised learning in cases where the data to be learned is neither sequential nor time-dependent. That is, the network compute a function f on fixed size input x such that

f(x)=y f(x) \ approx. y f(x)=y for training pairs (x,y) (y,x) (x,y).

The major limitation is that it is not capable of processing sequential data. For example, we cannot do speech recognition using a feedforward neural net – we need a recurrent neural network for this.

When we create a deeper feed forward neural network, we are giving the model the ability to capture more complex representations. This network is best used in image recognition tasks, Natural language processing, voice recognition tasks and many more.

The feedforward neural networks are applicable to many spaces where the classic machine learning techniques are applied; the major success of it has been in computer vision and speech recognition where the classification spaces are quite complicated. Also, these kind of neural networks are susceptible to noisy data and easy to maintain.