What is the neural network of backpropagation?

In the world of programming, computers and artificial intelligence is a neural network of backpropagation simply a type of artificial neural network (Ann), which uses backpropagation. Backpropagation is a basic and commonly used algorithm that instructs Ann to perform the task. Although this concept may seem confusing and, when looking at equations that are during the process, it seems completely alien to this concept, along with a complete neuron network, is relatively easy to understand.

For those who are not familiar with neural networks, Ann or simply NN, which means a "neural network", is a mathematical model that is patterned after certain properties of neural networks in real life, such as those found in living things. The human brain is a final neural network whose functioning provides certain traces of how to improve the structure and functioning of artificial LV. As the most basic brain, Ann has a network of interconnected artificialneurons that process information.

What is fascinating is that AnnIt can adapt and modify its structure if necessary, according to information that it receives from the environment and from the network. It is a sophisticated computational model that uses non -linear statistical data analysis and is able to interpret complex data relationships such as inputs and outputs. It can solve problems that cannot be solved using traditional computational methods.

The idea of ​​a neural network of backpropagation first appeared in 1969 from the work of Arthur E. Bryson and Yu-chi Ho. In later years, other programmers and scientists have improved this idea. Since 1974, the neural network of backpropagation has become recognized as an innovative breakthrough in the study and creating artificial neural networks.

The neural network learning is the main task within Ann, which ensures that it will continue to be able to process data properly and therefore properly perform its function. Neuron network backpropagation uses a generalized formDelta rules that allow the neural network learning. This means that it uses a teacher who is able to calculate the desired outputs from certain inputs supplied to the network.

In other words, the neural network of return processing learns by example. The programmer provides a learning model that shows what the right output would be due to the specific input set. This example of the input-output output is a teacher or model that other parts of the network can then patient calculations.

The whole process takes place methodically at measured intervals. Due to a certain set of Ann inputs, it will use the calculation obtained from the model to come with the initial output. It then compares this output with the originally known, expected or good output and performs the adjustment as needed. In this process, the error value is calculated. This then spreads back and forth through the neural network of backpropagation until the best possible output is determined.