Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a class of machine learning algorithms that
use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised).
are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher level features are derived from lower level features to form a hierarchical representation.
are part of the broader machine learning field of learning representations of data.
learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts.
Deep learning based on a set of algorithms that attempt to model high level abstractions in data. In a simple case, there might be two sets of neurons: one set that receives an input signal and one that sends an output signal. When the input layer receives an input it passes on a modified version of the input to the next layer. In a deep network, there are many layers between the input and the output (and the layers are not made of neurons but it can help to think of it that way), allowing the algorithm to use multiple processing layers, composed of multiple linear and non-linear transformations.
Automatic speech recognition
Natural language processing
Drug discovery and toxicology
Customer relationship management