## What is a Feedforward Neural Network?

A feedforward neural network is a Neural network of artificial neuronswhich has no feedback whatsoever. In such a network, the signals always run from the input layer towards the output layer. Multilayer perceptrons and radial basis function networks also belong to this class of networks. **Feedforward Neural Networks** are also referred to as **forward network** or as **forward-propagating network** is the term used. In contrast to such networks, networks with feedback are referred to as recurrent networks.

In a feedforward neural network, there are connections between nodes that do not form a cycle. **This network was the first and simplest artificial neural network**. The information in such a network always flows in one direction only. The flow of information comes from the input nodes through the hidden nodes (if any) to the output nodes. There are neither cycles nor loops in such a network. There are single-layer perceptrons or multi-layer perceptrons.

Depth **forward networks are part of Deep Learning**. This Deep Learning Models are intended to approximate some function and are called feedforward because the information flows through a function and is evaluated by x and then runs through intermediate calculations to define the function f and finally arrives at the output y. There are no feedback connections in which outputs of the model come back to themselves.

## Why is a neural model needed?

A network of perceptrons is used to solve a problem. For example, there are inputs to the network consisting of raw pixel data from a scanned, handwritten image of a digit. We want the network to learn weights and biases so that the network correctly classifies the digit in the output. We want it so that if there is a small change in the weighting, there is only a small change in the corresponding output from the network. This then makes learning possible.

## How does a feedforward neural network work?

In its simplest form, a feedforward neural network is considered a single-layer perceptron. In this model, a series of inputs are taken and multiplied by their weights. Each value is added to obtain a sum of the weighted input values. If the sum of values is above a specified threshold (usually set to zero), then the value produced will often be 1, whereas if the sum falls below the threshold, the output will be -1. The single-layer perceptron is very often used for classification tasks.