Difference between revisions of "Deep Learning"
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* [[Deep Belief Network (DBN)]] | * [[Deep Belief Network (DBN)]] | ||
* [[ResNet-50]] | * [[ResNet-50]] | ||
| − | + | * [http://medium.com/@gokul_uf/the-anatomy-of-deep-learning-frameworks-46e2a7af5e47 The Anatomy of Deep Learning Frameworks | Gokula Krishnan Santhanam] | |
http://www.global-engage.com/wp-content/uploads/2018/01/Deep-Learning-blog.png | http://www.global-engage.com/wp-content/uploads/2018/01/Deep-Learning-blog.png | ||
Revision as of 06:07, 11 July 2018
- Other Challenges of Machine Learning
- 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
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
