With the advancement in Machine Learning, Artificial Intelligence has taken a high road. Deep Learning is considered to be the most advanced technology built to solve complex problems that use massive data sets. This blog on what is a Neural Networks will introduce you to the basic concepts of Neural Networks and how they can solve complex data-driven problems.
Simple Definition Of A Neural Network:Modeled in accordance with the human brain, a Neural Network was built to mimic the functionality of a human brain. The human brain is a neural network made up of multiple neurons, similarly, an Artificial Neural Network (ANN) is made up of multiple perceptrons.
A neural network consists of three important layers:
Input Layer: As the name suggests, this layer accepts all the inputs provided by the programmer.
Hidden Layer: Between the input and the output layer is a set of layers known as Hidden layers. In this layer, computations are performed which result in the output.
Output Layer: The inputs go through a series of transformations via the hidden layer which finally results in the output that is delivered via this layer.
How Does A Neural Network Work?
To understand neural networks, we need to break it down and understand the most basic unit of a Neural Network, i.e. a Perceptron.
What Is A Perceptron?
A Perceptron is a single layer neural network that is used to classify linear data. It has 4 important components:Inputs,Weights and Bias,Summation Function,Activation or transformation Function.
The basic logic behind a Perceptron is as follows:
The inputs (x) received from the input layer are multiplied with their assigned weights w. The multiplied values are then added to form the Weighted Sum. The weighted sum of the inputs and their respective weights are then applied to a relevant Activation Function. The activation function maps the input to the respective output.
Why do we have to assign weights to each input?
Once an input variable is fed to the network, a randomly chosen value is assigned as the weight of that input. The weight of each input data point indicates how important that input is in predicting the outcome.
The bias parameter, on the other hand, allows you to adjust the activation function curve in such a way that a precise output is achieved.Summation Function
Once the inputs are assigned some weight, the product of the respective input and weight is taken. Adding all these products gives us the Weighted Sum. This is done by the summation function.
The main aim of the activation functions is to map the weighted sum to the output. Activation functions such as tanh, ReLU, sigmoid and so on are examples of transformation functions.