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.
CNNNetwork(*args, **kwargs)	Establish the network structure for a CNN.
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()	Establish the network structure for a MLP.
ResNetNetwork([n_residual_blocks, ...])	Establish the network structure for a ResNet.
TapNetNetwork([dropout, filter_sizes, ...])	Establish Network structure for TapNet.
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([n_filters, kernel_size, ...])	LITE Network.
AEBiGRUNetwork([latent_space_dim, n_layers, ...])	A class to implement an Auto-Encoder based on Bidirectional GRUs.