Deep learning heavily depends on a vast variety of neural network architectures to perform complex tasks. Frequent architectures comprise Convolutional Neural Networks (CNNs) for pattern recognition, Recurrent Neural Networks (RNNs) for time-series data processing, and Transformer networks for natural language processing. The choice of architecture