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Deep learning - Wikipedia #ril
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Deep learning (also known as DEEP STRUCTURED learning or HIERARCHICAL learning) is part of a broader family of NEURAL NETWORK METHODS based on CONVOLUTIONAL NEURAL NETWORKS (CNN)s ??.
Learning can be SUPERVISED, SEMI-SUPERVISED or UNSUPERVISED.
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Deep learning architectures such as deep neural networks, deep belief networks recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases SUPERIOR TO HUMAN EXPERTS.
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Neural networks were originally inspired by information processing and distributed communication nodes in BIOLOGICAL NERVOUS SYSTEMS yet have various differences from the structural and functional properties of biological brains (especially human brains), which make them incompatible with neuroscience evidences. Specifically, Neural Networks tend to be static and symbolic, while the human brain is dynamic and analog.
Definition
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Deep learning is a class of neural network algorithms that:
- Use a cascade of MULTIPLE LAYERS OF NONLINEAR PROCESSING units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input.
- Learn in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners.
- Learn multiple levels of REPRESENTATIONS that correspond to different levels of ABSTRACTION; the levels form A HIERARCHY OF CONCEPTS.
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