Difference between revisions of "Neural Architecture"
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* [[Automated Machine Learning (AML) - AutoML]] | * [[Automated Machine Learning (AML) - AutoML]] | ||
| − | * [[ | + | * [[Hyperparameter]]s Optimization |
* [[Other codeless options, Code Generators, Drag n' Drop]] | * [[Other codeless options, Code Generators, Drag n' Drop]] | ||
* [http://www.engadget.com/2019/03/22/mit-ai-automated-neural-network-design/ MIT’s AI can train neural networks faster than ever before | Christine Fisher - Engadget] | * [http://www.engadget.com/2019/03/22/mit-ai-automated-neural-network-design/ MIT’s AI can train neural networks faster than ever before | Christine Fisher - Engadget] | ||
Revision as of 05:03, 23 May 2019
NAS+machine+learning YouTube search... NAS+machine+learning ...Google search
- Automated Machine Learning (AML) - AutoML
- Hyperparameters Optimization
- Other codeless options, Code Generators, Drag n' Drop
- MIT’s AI can train neural networks faster than ever before | Christine Fisher - Engadget
- Neural Architecture Search (NAS) with Reinforcement Learning | Wikipedia
- Neural Architecture Search (NAS) with Evolution | Wikipedia
- Multi-objective Neural architecture search | Wikipedia
- Awesome NAS; a curated list
Various approaches to NAS have designed networks that are on par or even outperform hand-designed architectures. Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used:
- The search space defines which type of ANN can be designed and optimized in principle.
- The search strategy defines which strategy is used to find optimal ANN's within the search space.
- Obtaining the performance of an ANN is costly as this requires training the ANN first. Therefore, performance estimation strategies are used obtain less costly estimates of a model's performance. Neural Architecture Search | Wikipedia
