Deep learning networks

aeon networks are the models behind the deep learning estimators, and can be used in

their own right to construct bespoke solutions.

BaseDeepLearningNetwork([soft_dependencies, ...])	Abstract base class for deep learning networks.
TimeCNNNetwork([n_layers, kernel_size, ...])	Establish the network structure for a CNN.
EncoderNetwork([kernel_size, n_filters, ...])	Establish the network structure for an Encoder.
FCNNetwork([n_layers, n_filters, ...])	Establish the network structure for a FCN.
InceptionNetwork([n_filters, ...])	Inception Network.
MLPNetwork([use_bias])	Establish the network structure for a MLP.
ResNetNetwork([n_residual_blocks, ...])	Establish the network structure for a ResNet.
AEFCNNetwork([latent_space_dim, ...])	Establish the network structure for a AE-FCN.
AEResNetNetwork([latent_space_dim, ...])	Establish the network structure for a AE-ResNet.
LITENetwork([use_litemv, n_filters, ...])	LITE and LITE Multivariate (LITEMV) Networks.
AEBiGRUNetwork([latent_space_dim, n_layers, ...])	A class to implement an Auto-Encoder based on Bidirectional GRUs.
DisjointCNNNetwork([n_layers, n_filters, ...])	Establish the network structure for a DisjointCNN Network.
DCNNNetwork([latent_space_dim, n_layers, ...])	Establish the network structure for a DCNN-Model.
AEDCNNNetwork([latent_space_dim, ...])	Establish the Auto-Encoder based structure for a DCN Network.
AEAttentionBiGRUNetwork([latent_space_dim, ...])	A class to implement an Auto-Encoder based on Attention Bidirectional GRUs.
AEDRNNNetwork([latent_space_dim, ...])	Auto-Encoder based Dilated Recurrent Neural Networks (DRNN).