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= | + | [https://www.youtube.com/results?search_query=ai+Deep+Learning+Technique+Model YouTube] |
| − | [https://www.google.com/search?q= | + | [https://www.quora.com/search?q=ai%20Deep%20Learning%20Technique%20Model ... Quora] |
| + | [https://www.google.com/search?q=ai+Deep+Learning+Technique+Model ...Google search] | ||
| + | [https://news.google.com/search?q=ai+Deep+Learning+Technique+Model ...Google News] | ||
| + | [https://www.bing.com/news/search?q=ai+Deep+Learning+Technique+Model&qft=interval%3d%228%22 ...Bing News] | ||
* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning]] ... [[Deep Learning]] ... [[Learning Techniques]] | * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning]] ... [[Deep Learning]] ... [[Learning Techniques]] | ||
Revision as of 12:00, 28 April 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- Artificial Intelligence (AI) ... Machine Learning ... Deep Learning ... Learning Techniques
- 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
