NEURAL NETWORK

     A NEURAL NETWORK is a series of algorithms that try to recognize primary relationships in a set of data through a process that copies the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.

 

USES:

Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management. For example, ShareMarket.

                                                         

TYPES:

• Feedforward Neural Network – Artificial Neuron.
• Radial Basis Function Neural Network.
• Multilayer Perceptron
• Convolutional Neural Network. 
• Recurrent Neural Network(RNN) – Long Short Term Memory.
• Modular Neural Network.

HISTORY

The initial abstract base for modern neural networks was independently proposed by Alexander Bain in 1873 and William James in 1890. In their work, both thoughts and body activity resulted from interactions among neurons within the brain. Every activity led to the mechanism of a certain set of neurons. When activities were repeated, the connections between those neurons strengthened. According to his theory, this repetition was what led to the formation of memory. The general scientific community at the time was doubtful of Bain's theory because it required what appeared to be an excessive number of neural connections within the brain. It is now clear that the brain is most complex and that the same brain can handle multiple problems and inputs.

James's theory was similar to Bain's, however, he suggested that memories and actions resulted from electrical currents flowing among the neurons in the brain. His model, by focusing on the flow of electrical currents, did not require individual neural connections for each memory or action.

OVERVIEW:

A biological neural network is composed of groups of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. Connections, called synapses, are usually formed from axons to dendrites, though dendrodendritic synapses and other connections are possible. Apart from the electrical signal, there are other forms of signals that arise from neurotransmitter diffusion. Artificial intelligence, neural networks are information processing models inspired by the way biological neural systems process data. Artificial intelligence and cognitive modeling try to simulate some properties of biological neural networks. In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition, image analysis, and adaptive control, in order to construct software agents (in computer and video games) or autonomous robots. Historically, digital computers evolved from the von Neumann model, and operate via the execution of clear instructions via access to memory by a number of processors. On the other hand, the origins of neural networks are based on efforts to model information processing in biological systems. Unlike the von Neumann model, neural network computing does not separate memory and processing.

Neural network theory has served both to better identify how the neurons in the brain function and to provide the basis for efforts to create artificial intelligence.