Deep belief network in the context of Supervised learning

Deep belief network in the context of Supervised learning

Deep belief network Study page number 1 of 1

Play TriviaQuestions Online!

 or

Skip to study material about Deep belief network in the context of "Supervised learning"

⭐ Core Definition: Deep belief network

In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.

When trained without supervision on a set of examples, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors. After this learning step, a DBN can be further trained with supervision to perform classification.

↓ Menu
HINT:

In this Dossier

Deep belief network in the context of Deep learning

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 revolves 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. These architectures have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

View the full Wikipedia page for Deep learning
↑ Return to Menu
LINKED FROM (PARENTS)
← Deep learning
LINKS TO (CHILDREN)
Graphical model → Machine learning → Generative model → Artificial neural network → Latent variables → Unsupervised learning → Training set → Feature learning → Supervised learning → Statistical classification →