What is Backpropagation in Artificial Neural Networking?

An artificial neural network is a specialized type of AI system that combines a large number of small simple processors to simulate the organization of the human brain. Once the neural network has been created, the programmer must teach the system to function as desired with a training system such as backpropagation.

Neural Networking Basics

Neural networks are made to mimic the way that organic brains operate. Instead of neurons, however, an artificial neural net uses small on-or-off processors. Each decision point results in an on-state if the answer is positive, or an off-state if the answer is negative. An on-state causes the decision to pass along to a different point in the decision-making process. For a more detailed explanation of artificial neural networking read the article, Introduction to Neural Networks.

Once a neural network is set up, it’s just another computer system until it has a program to run. Artificially intelligent algorithms, however, are not like other computer programs. A regular computer program must have all tasks and variables hard-coded into the algorithm, but an AI program is able to “learn” new tasks and modify operations based on changing criteria. AI programs use a process called machine learning to develop the abilities necessary for any task. As you can read in Introduction to Machine Learning Approaches, this can come in the form of supervised learning, unsupervised learning, or a combination of the two.

What is Backpropagation?

Backpropagation is an odd-looking word that is used to describe the process of training an AI algorithm with a set of training data that includes a desired outcome. As the training data is put through the system, the neural net learns the correct way to process future data by observing the sample data set and outcome. With a program dependent on the training data to learn the correct way to process information, a more comprehensive set of training models will yield a better result.

Multi-level Neural Nets and Backpropagation

A multi-level neural net is a highly complex system that requires a special method of machine learning. Where a linear network can be corrected easily during training, since each decision point is visible, a multi-level network has hidden decision points that limit observation. By using backpropagation, AI programmers can teach the system how to process data, or evaluate data sets, without the need to observe and error-check the inner level’s decision points. The process of creating a backpropagation training algorithm is complicated, but flexibility of the resulting system allows the programmer of a multi-level neural net to create an infinitely modifiable system.

Neural Networking Basics

Neural networks are made to mimic the way that organic brains operate. Instead of neurons, however, an artificial neural net uses small on-or-off processors. Each decision point results in an on-state if the answer is positive, or an off-state if the answer is negative. An on-state causes the decision to pass along to a different point in the decision-making process. For a more detailed explanation of artificial neural networking read the article, Introduction to Neural Networks.

Once a neural network is set up, it’s just another computer system until it has a program to run. Artificially intelligent algorithms, however, are not like other computer programs. A regular computer program must have all tasks and variables hard-coded into the algorithm, but an AI program is able to “learn” new tasks and modify operations based on changing criteria. AI programs use a process called machine learning to develop the abilities necessary for any task. As you can read in Introduction to Machine Learning Approaches, this can come in the form of supervised learning, unsupervised learning, or a combination of the two.

What is Backpropagation?

Backpropagation is an odd-looking word that is used to describe the process of training an AI algorithm with a set of training data that includes a desired outcome. As the training data is put through the system, the neural net learns the correct way to process future data by observing the sample data set and outcome. With a program dependent on the training data to learn the correct way to process information, a more comprehensive set of training models will yield a better result.

Multi-level Neural Nets and Backpropagation

A multi-level neural net is a highly complex system that requires a special method of machine learning. Where a linear network can be corrected easily during training, since each decision point is visible, a multi-level network has hidden decision points that limit observation. By using backpropagation, AI programmers can teach the system how to process data, or evaluate data sets, without the need to observe and error-check the inner level’s decision points. The process of creating a backpropagation training algorithm is complicated, but flexibility of the resulting system allows the programmer of a multi-level neural net to create an infinitely modifiable system.

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