Neural Networks
A neural network is a machine learning model loosely inspired by the human brain, made of layers of connected nodes. Each node combines its inputs using learned weights and a nonlinear activation function, and the network learns by adjusting those weights to reduce its errors on example data.
A simple example is a model that predicts whether someone should play tennis today. The input nodes could be the temperature, the chance of rain, and the distance to the nearest court. The weights inside the network learn how much each factor matters and how they interact to produce the final likelihood.
Modern neural networks follow the same ideas at much larger scale, with architectures designed for specific kinds of data. Convolutional neural networks handle images, recurrent networks handle sequences, and transformers relate every part of an input to every other part. In manufacturing, neural networks power vision AI that inspects products and models that predict equipment failures.
Neural network and deep learning are closely related but not identical. A neural network can have just one or two layers; deep learning refers to neural networks with many stacked layers.