Interface administrators, Administrators (Semantic MediaWiki), Curators (Semantic MediaWiki), Editors (Semantic MediaWiki), Suppressors, Administrators
7,785
edits
Layer: Difference between revisions
no edit summary
(Created page with "{{see also|Machine learning terms}} ===Introduction to Layers in Machine Learning== Layers are a fundamental building block in artificial neural networks, machine learning algorithms modeled after the structure and function of the human brain. They perform computation on input data to produce outputs which can be used for making predictions or solving other problems. The number of layers within a neural network as well as its size and configuration determine its capacity...") |
No edit summary |
||
| Line 1: | Line 1: | ||
{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | |||
Layers are a fundamental building block in artificial neural networks, machine learning algorithms modeled after the structure and function of the human brain. They perform computation on input data to produce outputs which can be used for making predictions or solving other problems. The number of layers within a neural network as well as its size and configuration determine its capacity and expressive power. | Layers are a fundamental building block in artificial neural networks, machine learning algorithms modeled after the structure and function of the human brain. They perform computation on input data to produce outputs which can be used for making predictions or solving other problems. The number of layers within a neural network as well as its size and configuration determine its capacity and expressive power. | ||
| Line 36: | Line 36: | ||
Neuronal networks employ various layers, each with its own special properties and capabilities. Some of the most frequently employed layers include: | Neuronal networks employ various layers, each with its own special properties and capabilities. Some of the most frequently employed layers include: | ||
===Dense Layers== | ===Dense Layers=== | ||
Dense layers refer to neural networks in which each neuron is connected to every other. The output of each neuron in this layer is a weighted sum of its inputs, followed by application of activation function. Dense layers typically appear as hidden layers within neural networks. | Dense layers refer to neural networks in which each neuron is connected to every other. The output of each neuron in this layer is a weighted sum of its inputs, followed by application of activation function. Dense layers typically appear as hidden layers within neural networks. | ||
===Convolutional Layers== | ===Convolutional Layers=== | ||
Convolutional layers are mathematical constructs that apply a series of filters to input data, each one consisting of a small matrix of weights that convolve with each other to form feature maps. The output from each neuron in this layer is an ordered sum of activations from these feature maps, followed by application of an activation function. Convolutional layers are frequently employed in image recognition tasks. | Convolutional layers are mathematical constructs that apply a series of filters to input data, each one consisting of a small matrix of weights that convolve with each other to form feature maps. The output from each neuron in this layer is an ordered sum of activations from these feature maps, followed by application of an activation function. Convolutional layers are frequently employed in image recognition tasks. | ||
===Pooling Layers== | ===Pooling Layers=== | ||
Pooling layers are used to reduce the spatial dimension of input data by aggregating activations within a local neighborhood. The most popular type of pooling is max pooling, in which only the maximum activation from each neighborhood is retained and all others discarded. Pooling layers often work in conjunction with convolutional layers when performing image recognition tasks. | Pooling layers are used to reduce the spatial dimension of input data by aggregating activations within a local neighborhood. The most popular type of pooling is max pooling, in which only the maximum activation from each neighborhood is retained and all others discarded. Pooling layers often work in conjunction with convolutional layers when performing image recognition tasks. | ||
===Recurrent Layers== | ===Recurrent Layers=== | ||
Recurrent layers are algorithms that apply the same set of weights to input data at each time step, while also taking into account information from previous steps. They're commonly employed in sequence modeling tasks such as natural language processing or speech recognition. | Recurrent layers are algorithms that apply the same set of weights to input data at each time step, while also taking into account information from previous steps. They're commonly employed in sequence modeling tasks such as natural language processing or speech recognition. | ||