Introduction to Artificial Neural Network(ANN)
Parag Verma
28th Dec, 2019
Introduction to ANN
Whenever we discuss about Machine Learning(ML), there usually comes a point where we tend to talk about the model training part. This is the crux of any ML model as it executes the part which unearths the hidden nuancs in the data and derives insights.Right from Linear regression to Random forest, each algorithm essentially has that crux that skims through the data to identify pieces of generalisability in order to derive insights.Neural network are nothing different in the sense that they have an inherent algorithm that trains on the dataset and derive results. However,there is a lot of gung ho about the neural networks as it is able to solve problems with high level of accuracy and is particularly robust when it comes to text and image analysis.In this blog we will look at the essential building blocks of a basic neural network
Neurons as the basic Unit
Neural networks are modelled on neurons which facilitates transfer of messages within the nervous sytem.Each neuron recieves some input(stimulus),performs some computation and passes it along the chain to the other neuron.As shown in the below figure,millions of such neurons are connected to coordinate various activities within the human body.
Artifical neural network uses the same concept in the sense that it sets up a unit to mimic the neurons, enables some processing at it and then conveys the message/signal to the next neuron(layer).On a conceptual level, one can think of the below diagram to understand the basic building blocks of a neual network.
Lets discuss the neural network in the light of the most basic configuration known as Perceptron
Nodal Representation of ANN
The most simplest ANN configuration(also known as Perceptron) is shown below
