Artificial neural network: Difference between revisions
From Rice Wiki
(Created page with "An '''artificial neural network (ANN)''' is a machine learning algorithm that uses three layers of neurons to perform classification/regression. = Structure = All ANN's consist of three layers of neurons: the input layer, the hidden layer, and the output layer. The number of neurons in each layer determines the complexity of the model, with a larger layer indicating a more complicated model. Each neuron takes in information from neurons of the previous layer (and for t...") |
No edit summary |
||
| (2 intermediate revisions by the same user not shown) | |||
| Line 1: | Line 1: | ||
An '''artificial neural network (ANN)''' is a | An '''artificial neural network (ANN)''' is a [[neural network]] that uses three layers of neurons to perform classification/regression. | ||
= Structure = | = Structure = | ||
All ANN's consist of three layers of neurons: the input layer, the hidden layer, and the output layer. The number of neurons in each layer determines the complexity of the model, with a larger layer indicating a more complicated model. | All ANN's consist of three layers of neurons: the input layer, the hidden layer, and the output layer. The number of neurons in each layer determines the complexity of the model, with a larger layer indicating a more complicated model. | ||
As with other [[Neural network|neural networks]], each neuron takes in information from neurons of the previous layer (and for the case of the input layer, the input). An ANN is considered '''dense''' if each neuron takes input from ''every'' neuron of the previous layer. | |||
[[Category:Machine Learning]] | [[Category:Machine Learning]] | ||
Latest revision as of 23:52, 30 April 2024
An artificial neural network (ANN) is a neural network that uses three layers of neurons to perform classification/regression.
Structure
All ANN's consist of three layers of neurons: the input layer, the hidden layer, and the output layer. The number of neurons in each layer determines the complexity of the model, with a larger layer indicating a more complicated model.
As with other neural networks, each neuron takes in information from neurons of the previous layer (and for the case of the input layer, the input). An ANN is considered dense if each neuron takes input from every neuron of the previous layer.
