Neural Network Architectures

Neural network architectures are structured systems designed to process data, inspired by the human brain. They consist of interconnected nodes or "neurons," organized in layers: an input layer, hidden layers, and an output layer. Each neuron receives information, processes it, and passes it on. The depth and arrangement of these layers define the architecture, allowing the network to learn patterns from data, perform tasks like image recognition, and make predictions. Common types include feedforward networks, convolutional networks for images, and recurrent networks for sequences, each suited for specific applications in artificial intelligence.