Difference between revisions of "Deep Learning"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
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| − | [ | + | [https://www.youtube.com/results?search_query=deep+learning+Neural+Network YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=Neural+Network+deep+machine+learning+ML+artificial+intelligence ...Google search] |
* [[Other Challenges]] in Artificial Intelligence | * [[Other Challenges]] in Artificial Intelligence | ||
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* [[Deep Belief Network (DBN)]] | * [[Deep Belief Network (DBN)]] | ||
* [[ResNet-50]] | * [[ResNet-50]] | ||
| − | * [ | + | * [https://medium.com/@gokul_uf/the-anatomy-of-deep-learning-frameworks-46e2a7af5e47 The Anatomy of Deep Learning Frameworks | Gokula Krishnan Santhanam] |
* [[Hierarchical Temporal Memory (HTM)]] | * [[Hierarchical Temporal Memory (HTM)]] | ||
* [[Deep Features]] | * [[Deep Features]] | ||
| − | + | https://www.global-engage.com/wp-content/uploads/2018/01/Deep-Learning-blog.png | |
| − | 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” [ | + | 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” [https://www.cs.toronto.edu/~hinton/csc321/readings/tics.pdf Learning Multiple Layers of Representation | Geoffrey Hinton] |
<youtube>M8qdcOxDxgA</youtube> | <youtube>M8qdcOxDxgA</youtube> | ||
Revision as of 09:08, 28 March 2023
YouTube search... ...Google search
- 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
