Much of the most important work being done in the field of neural net models, and indeed machine learning in general, is using very complex neural nets with many layers and features. This approach is often called deep architecture or deep learning. Human and animal learning occurs at a rate and depth that no machine can match. Many of the elements of biological learning still remain a mystery. One of the key areas of research, and one of the most useful in practical applications, is that of object recognition. This is something quite fundamental to living systems, and higher animals have evolved an extraordinary ability to learn complex relationships between objects. Biological brains have many layers; each synaptic event exists in a long chain of synaptic processes. In order to recognize complex objects, such as people's faces or handwritten digits, a fundamental task that is needed is to create a hierarchy of representation from the raw input to higher and...
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