In machine learning, deep learning focuses on utilizing multilayered
neural networks to perform tasks such as classification, regression, and
representation learning. The field takes inspiration from biological
neuroscience and is centered around stacking artificial neurons into layers
and "training" them to process data. The adjective "deep" refers to the use of
multiple layers (ranging from three to several hundred or thousands) in the
network. Methods used can be supervised, semi-supervised or unsupervised.
Some common deep learning network architectures include fully connected
networks, deep belief networks, recurrent neural networks, convolutional
neural networks, generative adversarial networks, transformers, and neural
radiance fields.