Difference between revisions of "Deep Learning"
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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] | + | Deep learning models are vaguely inspired by information processing and [[Agents#Communication | 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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Revision as of 13:21, 12 February 2023
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- Other Challenges in Artificial Intelligence
- Deep Neural Network (DNN)
- (Deep) Convolutional Neural Network (DCNN/CNN)
- (Deep) Residual Network (DRN) - ResNet
- Deep Belief Network (DBN)
- ResNet-50
- The Anatomy of Deep Learning Frameworks | Gokula Krishnan Santhanam
- Hierarchical Temporal Memory (HTM)
- Deep Features
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
