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 contro ...
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
In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function is an activation function defined as the positive part of its argument:
: f(x) = x^+ = \max(0, x),
where ''x'' is the input to a neu ...
layer in vanilla neural network.
RNN RNN or rnn may refer to:
* Random neural network, a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals
* Recurrent neural network, a class of artificial neural networks where connections betw ...
layer in the
RNN RNN or rnn may refer to:
* Random neural network, a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals
* Recurrent neural network, a class of artificial neural networks where connections betw ...
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
Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.
De ...
layering and
neocortical layering: deep learning layering depends on
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 contro ...
, 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, siz ...
.
Dense layer
Dense layer, also called fully-connected layer, refers to the layer whose inside neurons connect to every neuron in the preceding layer.
See also
*
Deep Learning
Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.
De ...
*
Neocortex#Layers
References
{{Differentiable computing
Artificial neural networks