Difference between revisions of "Deep Learning"
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| + | [http://www.youtube.com/results?search_query=deep+learning+Neural+Network YouTube search...] | ||
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| + | Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains, which make them incompatible with neuroscience evidences. “Deep Learning is an algorithm which has no theoretical limitations of what it can learn; the more data you give and the more computational time you provide, the better it is” [http://www.cs.toronto.edu/~hinton/csc321/readings/tics.pdf Learning Multiple Layers of Representation | Geoffrey Hinton] | ||
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| + | == The Wall == | ||
| + | [http://www.youtube.com/results?search_query=challenges+deep+learning+Neural+Network YouTube search...] | ||
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| + | Deep neural nets are huge and bulky inefficient creatures that allow you to effectively solve a learning problem by getting huge amounts of data and a super computer. They currently trade efficiency for brute force almost every time. | ||
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Revision as of 11:52, 24 June 2018
Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains, which make them incompatible with neuroscience evidences. “Deep Learning is an algorithm which has no theoretical limitations of what it can learn; the more data you give and the more computational time you provide, the better it is” Learning Multiple Layers of Representation | Geoffrey Hinton
The Wall
Deep neural nets are huge and bulky inefficient creatures that allow you to effectively solve a learning problem by getting huge amounts of data and a super computer. They currently trade efficiency for brute force almost every time.
