Neural Network

Neural Network 

Neural network more properly known as  'Artificial neural network(ANN)'  is simply a circuit of neurons. This is a kind of problem solving strategy where neurons act as the main building element. A large number of highly interconnected neurons is composed in order to solve specific problem.

What is Neural network?

       Neural network is an information processing paradigm that inspired by the biological aspects like nervous system, the brain processing information.

The term network is used to any system of artificial neurons. This network may range from a single neuron to a large group of neurons where each neuron is highly interconnected to each other in the network.

simple picture of neural network

How artificial neural network works?

Neural Network consists of processors which are arranged in a levels. First level is containing input information. Then each successive level receives the output from the level preceding it. Neurons further receive the signals from optic nerves closer to it. Final level produces the output of the systems. 

When the input is flowing from layer to layer each node weights the importance input from each of its predecessors. Inputs that give the right answer are weighted higher. Hence this contributes to grab the right solution.

Types of Neural Network

Neural network is almost introduced with term deep learning. There are variations in classic neural network according to the number of hidden nodes the model has. This variation of inputs and outputs of each node allows different designs in the propagation of information among layers.

Feed-forward neural network-  this is a type of ANN. In this type information is directly passed to output layer from input layer or the precedence layer. There may be some hidden nodes but in this type the function is more simple and efficient.

Recurrent neural network (RNN) -  this is type of ANN where connections between nodes form a directed graph along a sequence. 

Unlike feed-forward NN, RNN can use their internal state to process sequence of inputs.

Conventional neural network- this is a class of deep neural network(CNN/conNet). Commonly used in analyzing visual imagery. This type of  NN has been used in most advanced and useful applications such as facial recognition, text digitization and neural language processing. 

Applications of Neural Network

• Image recognition 

    this ability has influenced for many exciting and transformational innovations in computer vision, speech recognition and the best example is self-driving cars.

• Forecasting

  specially in business field/prices in stock market.

Unlike many other prediction techniques Artificial neural network doesn't impose any rules or restrictions on input variables.

This is only some basic knowledge about neural network. We must learn more about neural network as AI is becoming increasingly popular throughout the world.