23. Deep Learning, Neural Networks

Deep learning is a special branch of machine learning using a collage of algorithms to model high-level data motifs. Deep learning resembles the biological communications of systems of brain neurons in the central nervous system (CNS), where synthetic graphs represent the CNS network as nodes/states and connections/edges between them. For instance, in a simple synthetic network consisting of a pair of connected nodes, an output sent by one node is received by the other as an input signal. When more nodes are present in the network, they may be arranged in multiple levels (like a multiscale object) where the ith layer output serves as the input of the next (i + 1)st layer. The signal is manipulated at each layer, sent as a layer output downstream, interpreted as an input to the next, (i + 1)st layer, and so forth. Deep learning relies on multipler layers of nodes and many edges linking the nodes forming input/output (I/O) layered grids representing a multiscale processing network. At each layer, linear and non-linear transformations are converting inputs into outputs.