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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]]: reduce the dimensions of the feature maps.
 
===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.
 
===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.


===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 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.


==How Layers Work in Neural Networks==
==How Layers Work in Neural Networks==
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