Difference between revisions of "Deep Learning"
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* [[Other Challenges]] in Artificial Intelligence | * [[Other Challenges]] in Artificial Intelligence | ||
* [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]] | * [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]] | ||
| − | * [[(Deep) Convolutional Neural Network (DCNN/CNN)]] | + | ** [[(Deep) Convolutional Neural Network (DCNN/CNN)]] |
| − | * [[(Deep) Residual Network (DRN) - ResNet]] | + | ** [[(Deep) Residual Network (DRN) - ResNet]] |
| − | * [[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] | * [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)]] | ||
Revision as of 14:06, 9 May 2023
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- Artificial Intelligence (AI) ... Machine Learning (ML) ... Deep Learning ... Learning Techniques
- Other Challenges in Artificial Intelligence
- Deep Neural Network (DNN)
- 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
