Deep Neural Network (DNN) Architecture

Context:
- It can have DNN Architecture Properties, such as:
  - DNN Depth, which refers to the number of layers in the network.
  - DNN Activation Function, which determines the output of a neuron given its input.
  - DNN Loss Function, used during the training phase to optimize the model's parameters.
  - DNN Training Algorithm, the algorithm often used for training deep neural networks.
  - DNN Information Flow, such as: Feed-Forward DNN Architecture and Feedback DNN Architecture.
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Example(s):
- A Convolutional Neural Network (CNN) Architecture (for CNN models), which uses a series of convolutional layers to extract spatial features from images.
- A Recurrent Neural Networks (RNN) Architecture (for RNN models), which uses recurrent connections to learn long-term dependencies in sequential data.
- A Transformer Neural Network Architecture (for Transformer models), which uses attention mechanisms to process sequences of data in parallel rather than sequentially.
  - A Bidirectional Encoder Representations from Transformers (BERT)-based arch.
- A ResNet Architecture (for ResNet models), which uses residual connections to facilitate the training of very deep networks.
- A Autoencoder Architecture, which uses only one or two layers and are not capable of capturing complex patterns in data.
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Counter-Example(s):
- Shallow Neural Network Architecture.
See: Model Architecture, Machine Learning Algorithm, Hyperparameter Tuning.

References