neural network architectures

What is neural network architectures?

Neural network architectures refer to the various ways in which artificial neural networks are structured and organized. Different architectures are designed to excel at specific tasks by employing different types of layers, connections, and activation functions. Common architectures include: Feedforward Neural Networks (simple, unidirectional data flow), Convolutional Neural Networks (CNNs, used for image and video processing), Recurrent Neural Networks (RNNs, used for sequential data like text and time series), Transformers (used for natural language processing and other sequence-to-sequence tasks), and Generative Adversarial Networks (GANs, used for generating new data that resembles training data). Each architecture has its strengths and weaknesses, influencing its applicability to diverse problems like image classification, natural language understanding, and data generation.