# A Quick Introduction to Neural Networks

This article provides a beginner level introduction to multilayer perceptron and backpropagation.

**By Ujjwal Karn.**

An Artificial Neural Network (ANN) is a computational model that is inspired by the way biological neural networks in the human brain process information. Artificial Neural Networks have generated a lot of excitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing. In this blog post we will try to develop an understanding of a particular type of Artificial Neural Network called the Multi Layer Perceptron.

#### A Single Neuron

The basic unit of computation in a neural network is the **neuron**, often called a **node** or **unit**. It receives input from some other nodes, or from an external source and computes an output. Each input has an associated **weight** (w), which is assigned on the basis of its relative importance to other inputs. The node applies a function ** f **(defined below) to the weighted sum of its inputs as shown in Figure 1 below:

###### Figure 1: a single neuron

The above network takes numerical inputs **X1** and **X2** and has weights **w1** and **w2** associated with those inputs. Additionally, there is another input **1** with weight **b** (called the **Bias**) associated with it. We will learn more details about role of the bias later.

The output **Y** from the neuron is computed as shown in the Figure 1. The function ** f **is non-linear and is called the

**Activation Function**. The purpose of the activation function is to introduce non-linearity into the output of a neuron. This is important because most real world data is non linear and we want neurons to

*learn*these

*non linear representations.*

Every activation function (or *non-linearity*) takes a single number and performs a certain fixed mathematical operation on it [2]. There are several activation functions you may encounter in practice:

**Sigmoid:**takes a real-valued input and squashes it to range between 0 and 1

σ(x) = 1 / (1 + exp(−x))

**tanh:**takes a real-valued input and squashes it to the range [-1, 1]

tanh(x) = 2σ(2x) − 1

**ReLU**: ReLU stands for Rectified Linear Unit. It takes a real-valued input and thresholds it at zero (replaces negative values with zero)

f(x) = max(0, x)

The below figures [2] show each of the above activation functions.

###### Figure 2: different activation functions

**Importance of Bias:** The main function of Bias is to provide every node with a trainable constant value (in addition to the normal inputs that the node receives). See this link to learn more about the role of bias in a neuron.

#### Feedforward Neural Network

The feedforward neural network was the first and simplest type of artificial neural network devised [3]. It contains multiple neurons (nodes) arranged in **layers**. Nodes from adjacent layers have **connections** or **edges** between them. All these connections have **weights** associated with them.

An example of a feedforward neural network is shown in Figure 3.