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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
[[ | In [[machine learning]], a [[layer]] is a set of [[neurons]] ([[node]]s). [[Neural network]]s consist of multiple interconnected layers that work together to process [[input]] [[data]] and make predictions. Each [[layer]] performs specific computations on the input data, and its output serves as input for the subsequent layer. | ||
*[[Neuron]]s form [[layer]]s which form [[neural network]]s. | |||
==Types of Layers in Neural Networks== | ==Types of Layers in Neural Networks== | ||
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*[[Recurrent layer]]: Recurrent neural networks (RNNs) employ layers with a memory mechanism that enables them to process sequences of data and recognize temporal dependencies. | *[[Recurrent layer]]: Recurrent neural networks (RNNs) employ layers with a memory mechanism that enables them to process sequences of data and recognize temporal dependencies. | ||
*[[Dense layer]] ([[Fully connected layer]]): Layers in which every neuron is connected to every neuron in the previous layer. | *[[Dense layer]] ([[Fully connected layer]]): Layers in which every neuron is connected to every neuron in the previous layer. | ||
*[[Pooling layer]]: | *[[Pooling layer]]: reduces the spatial dimensions of the input while retaining important information in the data. It applies an aggregation function, such as max or average pooling, to small rectangular regions of the input data, resulting in an output with fewer dimensions. Commonly used in [[CNN]]. | ||
==How Layers Work in Neural Networks== | ==How Layers Work in Neural Networks== | ||