Deep learning for pattern learning and recognition

摘要

Summary form only given. Deep learning is a set of algorithms in machine learning that attempt to learn in multiple levels, corresponding to different levels of abstraction. It is typically used to abstract useful information from data. The levels in these learned statistical models correspond to distinct levels of concepts, where higher-level concepts are defined from lower-level ones, and the same lower level concepts can help to define many higher-level concepts. Alternatively, the main advantage of deep learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. This talk is to overview the foundationa, data representation capability of deep networks, and to investigate efficient deep learning algorithms, and meaningful applications.