Neural Networks

 What's in it for you ?

• What is neural networks ?
• What can neural network do?
• How does neural network works ?
• Types of neural network.
• Use case.
  

 

Hi guys! I heard you what you know what neural network is ? As a matter of fact, you have been using neural network on daily basis. When you ask your mobile assistant to perform search for you..

Self driving car use it.

Computer game use it.

Also use in processing the map image on your phone.

What is neural network?

"Neural network is a system or hardware that is designed to operate like human brain."

What can neural network do?

let's list out the things neural network can do for you. 

• Translate text
• Identify face
• recognize speech
• Read handwritten text
• Control robots 
• And lot of other things..

How does neural network works? 

There are different layer of a neural network.

 Input layer : pick up input signal and passes them to the next layer.

Hidden layer : does all kind of calculation and feature extraction.

Output layer : This layer deliver the final result.

Let's consider the image of this vehicle and find out what's on the number plate. 28*28 pixels of the imge is fed as input to identify registeration plate. Each nuran has a number called activation that represents the grayscale value of the corresponding pixel ranging from 0 to 1.1 for white pixel and for black pixel. Each neuron is lit up when it's activation is close to 1. pixel in the from of array are fed to the input layer. let's name the input x1, x2 and x3 respectively. Input layer passes it to hidden layer. The interaction are assigned weight at random. The weight are multilied with the input signal and a bias is added to all of them.

The weighted sum of input is fed as to the activation function to decide which nodes to fire for feature extraction. As the siganal flows within the hidden layers, The weighted sum of input is calculated and fed the activation function in each layer to decide which nodes to fire. Ther are different activation funtions:

• Sigmoid Function: used when the model is predicting probobility.
• Thresold Function: used when the output depend on a tresold values.
• Relu Function: It gives x if x is positive, otherwise.
• Hyperbolic tangent Function: Similar to sigmoid function with a range of (-1, 1).

Finally , The model would predict the outcomes by applying suitable activation function to output layer. identifies the number plate. Optical character recognition (OCR) is used on the the image to convert it into text in order to identify what's written on the plate. Error in th output is backpropogated through the network and weightd are adjusted to minimize the error rate. This is calculated by a cost function.

Types of Neural Network:

• Feedforword Neural Network: 

simplest form of ANN data travels only in one direction (input-output). 

Application - vision and speech recognition.

• Radial Basis Function Neural Network:

This model classifies the data point based on it's distance from a center point.

Application - power restoration system.

• Kohonen Self Organizing Neural Network:

Vector of random diamentions are input to discrete map comprised of neurons.

Application - used to recognize patterns in data like in medical analysis.

• Recurrent Neural Network:

The hidden layer saves its output to be used for future prediction.

Application - text to speech conversion model.

• Convolution Nural Network:

The input features are taken in batches like a filter. This allows the network to remember an image parts.
Application - used in signal and image processing.

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• Modular Neural Network:

It has collection of different neural netwoks working together to get output.
Application - still under-going research.