A neural net is a type of deep learning system used to approximate complex functions and map inputs to outputs for tasks like pattern recognition, classification, prediction or regression. It's composed of thousands or even millions of interconnected artificial neurons that are grouped together in layers. Each subsequent layer feeds its output as input to the next layer in a hierarchical fashion creating something akin to an smart inference cascade! It comprises various weight-scaling parameters distributively connecting nodes, coupling circuits cognitively attaching together where given suggestions learn via systemic training thereby impacting operating weights such into fuzzy decisions.. At its core node perceptions project into nonlinear function-learning filtering culminating recent machine&deep learning uprising many carrying-weights can be adjusted induvidually dramatically altering their decision-making outputs .