Neural Networking

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Neural Networking

Neural Networking

Mostly known as the artificial neural network (ANN) is best explained as a series of algorithms based on biological simulations performed on the computer. The processing network is modelled inspired by the brain’s neuronal structure but on a smaller scale compared to our brain neuronal system. These networks have the ability to adjust to changing input, so the resulting output doesn’t need to redesign.

What is Neural Networking?
A neural network mimics the way a human brain operates, to attempt to proceed a right connection. These connection are made with the help of neurons. The human brain is approximately filled with 86 billion nerve cells (neuron). A basic structure of neuron consists of axon and dendrites. The body of a neuron is called axons, they connect other neurons. The information exchanger in form of tentacles are called as dendrite creates electric impulses. A neuron decides if the message needs to be forwarded or not.

A human neuron consisting of axon and dendrites.

Types of Neural Networks:
There are different types of neural networks, all of them has their special cases and complexity levels. Following are the two most basic and used neural networks-
1. Feedforward neural network: The most basic of all the ANNs, in this network travels in one direction from input to output.
2. Recurrent neural network: the most used and widely accepted, in which data can flow in multiple directions. These neural networks are applied to complex tasks such as learning handwriting or language recognition.
Convolutional neural networks, Hopfield networks, Boltzmann machine networks are some of the other networks. Choosing the right network mainly depends on the task, the data that you are training and the output.

• Neural networks are basically used to extract patterns and trends that are complex to be detected by a human brain or even by using computer techniques. Whereas a trained neural network can be termed as “expert” which can analyze the given information. This particular expert will help you guide and provide a different projection with a new interest and clear all your query. Other applications for neural network are as follows:
• Real-time operation: There are special computer hardware devices which are designed and manufactured which can be used to its capabilities.
• Adaptive learning: A step by step process to learn a performed task based on the data given for training or pre-training experience.
• Self-organization: An artificial neural network can create its own self-organization of the given information it received during the input time.
• ANN system can save us from major network damage without degrading the performance.

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