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 Neural networks consist of several layers, each with its own specific properties and functions. Common examples of layers include:
-- Input Layers: The initial layer in a neural network that takes input data and passes it along to the next one.
+*[[Input layer]]: The initial layer in a neural network that takes input data and passes it along to the next one.
+*[[Hidden layer]]: Sublayers situated between the input and output layers that perform intermediate computations on input data.
-- Hidden Layers: Sublayers situated between the input and output layers that perform intermediate computations on input data.
+*[[Output layer]]: The final layer in a neural network that generates the final output based on the intermediate computations performed by the hidden layers.
+*[[Convolutional layer]]: Used in convolutional neural networks (CNNs), these layers perform convolution operations on input data to extract features and reduce spatial dimensions.
-- Output Layers: The final layer in a neural network that generates the final output based on the intermediate computations performed by the hidden layers.
+*[[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.
-- Convolutional Layers: Used in convolutional neural networks (CNNs), these layers perform convolution operations on input data to extract features and reduce spatial dimensions.
+*[[Pooling layer]]:
-- Recurrent Layers: Recurrent neural networks (RNNs) employ layers with a memory mechanism that enables them to process sequences of data and recognize temporal dependencies.
-- Fully Connected Layers: Layers in which every neuron is connected to every neuron in the previous layer.
 ===Dense Layers===