A layer in a deep learning model is a structure or

network topology Network topology is the arrangement of the elements ( links, nodes, etc.) of a communication network. Network topology can be used to define or describe the arrangement of various types of telecommunication networks, including command and contr ...

in the model's architecture, which takes information from the previous layers and then passes it to the next layer. There are several famous layers in deep learning, namely convolutional layer and maximum pooling layer in the

convolutional neural network In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Netwo ...

. Fully connected layer and ReLU layer in vanilla neural network. RNN layer in the RNN model and deconvolutional layer in

autoencoder An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). The encoding is validated and refined by attempting to regenerate the input from the encoding. The autoencoder lear ...

etc.

Differences with layers of the neocortex

There is an intrinsic difference between deep learning layering and neocortical layering: deep learning layering depends on

, while neocortical layering depends on intra-layers

homogeneity Homogeneity and heterogeneity are concepts often used in the sciences and statistics relating to the uniformity of a substance or organism. A material or image that is homogeneous is uniform in composition or character (i.e. color, shape, size, ...

Dense layer

Dense layer, also called fully-connected layer, refers to the layer whose inside neurons connect to every neuron in the preceding layer.

References

{{Differentiable computing Artificial neural networks

Differences with layers of the neocortex

Dense layer

See also

References