A layer in a deep learning model is a structure or

network topology Network topology is the arrangement of the elements (Data link, links, Node (networking), nodes, etc.) of a communication network. Network topology can be used to define or describe the arrangement of various types of telecommunication networks, ...

in the model's architecture, which takes information from the previous layers and then passes it to the next layer.

Layer types

The first type of layer is the Dense layer, also called the fully-connected layer, and is used for abstract representations of input data. In this layer, neurons connect to every neuron in the preceding layer. In multilayer perceptron networks, these layers are stacked together. The Convolutional layer is typically used for image analysis tasks. In this layer, the network detects edges, textures, and patterns. The outputs from this layer are then fed into a fully-connected layer for further processing. See also: CNN model. The Pooling layer is used to reduce the size of data input. The Recurrent layer is used for text processing with a memory function. Similar to the Convolutional layer, the output of recurrent layers are usually fed into a fully-connected layer for further processing. See also: RNN model. The Normalization layer adjusts the output data from previous layers to achieve a regular distribution. This results in improved scalability and model training. A Hidden layer is any of the layers in a

Neural Network A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or signal pathways. While individual neurons are simple, many of them together in a network can perfor ...

that aren't the input or output layers.

Differences with layers of the neocortex

There is an intrinsic difference between

deep learning Deep learning is a subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience a ...

layering and neocortical layering: deep learning layering depends on

, while neocortical layering depends on intra-layers homogeneity.

References

{{Differentiable computing Artificial neural networks