Deep learning
Encyclopedia
Deep learning is a sub-field within machine learning
that uses deep architectures to model complex relationships among data. Such models have proven to be effective feature extractors over high-dimensional, structured data (Hinton – Scholarpedia, 2009).
One of the earliest successful implementations of a deep model (Hinton et al. 2006) involves learning the distribution of high level image (or possibly other data) features using successive layers of binary latent variables. However, real valued variables may also be used. The approach uses a restricted Boltzmann machine (Smolensky, 1986) to model each new layer of higher level features. Each new layer guarantees an increase on the lower-bound of the log likelihood of the data, thus improving the model, if trained properly. Once sufficiently many layers have been learned the deep architecture may be used as a generative model by reproducing the data when sampling down the model (an "ancestral pass") from the top level feature activations.
Machine learning
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...
that uses deep architectures to model complex relationships among data. Such models have proven to be effective feature extractors over high-dimensional, structured data (Hinton – Scholarpedia, 2009).
One of the earliest successful implementations of a deep model (Hinton et al. 2006) involves learning the distribution of high level image (or possibly other data) features using successive layers of binary latent variables. However, real valued variables may also be used. The approach uses a restricted Boltzmann machine (Smolensky, 1986) to model each new layer of higher level features. Each new layer guarantees an increase on the lower-bound of the log likelihood of the data, thus improving the model, if trained properly. Once sufficiently many layers have been learned the deep architecture may be used as a generative model by reproducing the data when sampling down the model (an "ancestral pass") from the top level feature activations.