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What Is Neural Network Architecture?

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  • Written By: K.C. Bruning
  • Edited By: John Allen
  • Last Modified Date: 28 September 2014
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Neural network architecture uses a process similar to the function of a biological brain to solve problems. Unlike computers, which are programmed to follow a specific set of instructions, neural networks use a complex web of responses to create their own sets of values. The system works primarily by learning from examples and trial and error. Overall, neural network architecture takes the process of problem-solving beyond what humans or conventional computer algorithms can process.

The concept of neural network architecture is based on biological neurons, the elements in the brain that implement communication with the nerves. These are simulated in the computational environment by programs composed of nodes and values that work together to process data. This method is meant to compensate for the inability of typical computer algorithms to process simple aural and visual data as easily as humans. It also strives to improve upon human ability by increasing the speed and efficiency of the process.

A typical system of neural network architecture will attempt to solve a problem by asking a series of yes and no questions about the subject. By discarding certain elements and accepting others, an answer is eventually found. This process is similar to the way a biological brain would solve a problem, but it can be engineered to work in a faster and more complex manner by focusing on a specific area.

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As neural network architecture is constructed so that the program will develop its own method of solving a problem, it can be unpredictable. This can often be beneficial, as a less defined process may develop answers that human minds are incapable of devising on their own. It can also be problematic, as there is no way of tracking the specific steps the computer takes to solve the problem and thus fewer ways of troubleshooting any problems that may arise during or after the process is run.

One of the benefits of neural network architecture is that by continually learning from trial and error, the system can improve its problem-solving ability. Over time, this can increase the network’s ability to detect patterns and process unorganized and indistinct bodies of data. This process can be engineered for anything from a single process to a wide array of interconnected elements.

While neural network architecture can be engineered to focus on certain areas, it cannot be restricted to specific tasks. In order for the system to be effective, it must be given the elements necessary to troubleshoot on its own. Without the proper materials, the answers the system generates will usually not be satisfactory.

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