Neuralnet
Neuralnet is a broad term used to describe a family of computational models inspired by the structure and function of biological neural networks. A neuralnet consists of interconnected units called neurons or nodes, organized into layers. Each connection carries a weight that modulates the signal passed between neurons. An input vector is transformed through the network to produce an output, with the transformation learned from data.
In a typical neuralnet, information flows forward from input to output in a feedforward fashion, though recurrent
Many neuralnets are deep, containing multiple hidden layers. Variants include convolutional neural networks for structured grid
Applications span computer vision, natural language processing, speech recognition, time-series forecasting, and control systems. Neuralnets have
Limitations include data requirements, risk of overfitting, and lack of interpretability. Training can be sensitive to