Deep belief networks (DBNs) are neural networks made of several stacked layers that are first pre-trained layer by layer without supervision, usually as so-called restricted Boltzmann machines. Geoffrey Hinton and colleagues introduced the method in 2006; it showed that deep networks could be trained efficiently and thus helped the rise of deep learning. In practice, DBNs have since been largely replaced by other architectures.
Deep learning is a branch of machine learning that uses artificial neural networks with many layers. Such networks learn from large amounts of data which features matter – from edges in images to relationships of meaning in texts. Deep learning underpins today's language models, image and speech recognition and machine translation, and usually requires considerable computing power and training data.
Artificial neural networks (ANNs) are computational models made up of many connected units (“neurons”) arranged in layers that process inputs via weighted connections. During training, the weights are adjusted step by step using examples, usually by backpropagation. ANNs are the foundation of deep learning and power image recognition, speech recognition and large language models. Despite their biological inspiration, they work far more simply than the human brain.
In unsupervised learning, a model receives data without predefined correct answers and searches for structures on its own. Typical methods are clustering, which groups similar records, and dimensionality reduction, which condenses many features into a few essential ones. Organisations use it to discover customer segments or spot unusual transactions; the groups found must then be interpreted by domain experts.
