BASIC OF NEURAL NETWORK

 Neural networks and deep learning are big topics in Computer Science and in the technology industry, they currently provide the best solutions to many problems in image recognition, speech recognition and natural language processing.The simplest definition of a neural network, more properly referred to as an 'artificial' neural network (ANN), is provided by the inventor of one of the first neurocomputers Dr. Robert Hecht-Nielsen. He defines a neural network as:

"...a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.

In "Neural Network Primer: Part I" by Maureen Caudill, AI Expert, Feb. 1989

Or you can also think of Artificial Neural Network as computational model that is inspired by the way biological neural networks in the human brain process information.Neural networks are typically organized in layers. Layers are made up of a number of interconnected 'nodes' which contain an 'activation function'. Patterns are presented to the network via the 'input layer', which communicates to one or more 'hidden layers' where the actual processing is done via a system of weighted 'connections'. The hidden layers then link to an 'output layer' where the answer is output as shown in the graphic below. 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.

          What is an Artificial Neural Networks? | Bernard Marr

Although there are many different kinds of learning rules used by neural networks, this demonstration is concerned only with one; the delta rule. The delta rule is often utilized by the most common class of ANNs called 'backpropagational neural networks' (BPNNs). Backpropagation is an abbreviation for the backwards propagation of error.   

 Application of Neural Networks 

 Application of Neural Network | Top 3 Application of Neural Network

Neural networks are broadly used, with applications for financial operations, enterprise planning, trading, business analytics, and product maintenance. Neural networks have also gained widespread adoption in business applications such as forecasting and marketing research solutions, fraud detection, and risk assessment.

Facial Recognition

Neural networks are playing a significant role in facial recognition. Some smartphones can identify the age of a person. This is based on facial features and visual pattern recognition.

Weather Forecasting

Neural networks are trained to recognize the patterns and identify distinct kinds of weather. Weather forecasting, with the help of neural networks, not only predicts the weather.

Music composition

Neural networks are mastering patterns in sounds and tunes. These networks train themselves adequetly to create new music. They are also being used in music composition software.

 Social Media

No matter how cliche it may sound, social media has altered the normal boring course of life. Artificial Neural Networks are used to study the behaviours of social media users. Data shared everyday via virtual conversations is tacked up and analyzed for competitive analysis. 

 

 

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