Difference between revisions of "Neural Architecture"
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* [[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] | ||
| + | * [http://en.wikipedia.org/wiki/Neural_architecture_search#NAS_with_Reinforcement_Learning NAS with Reinforcement Learning | Wikipedia] | ||
| + | * [http://en.wikipedia.org/wiki/Neural_architecture_search#NAS_with_Evolution Neural Architecture Search (NAS) with Evolution | Wikipedia] | ||
| + | * [http://en.wikipedia.org/wiki/Neural_architecture_search#Multi-objective_Neural_architecture_search Multi-objective Neural architecture search | Wikipedia] | ||
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: | 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: | ||
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* 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. [http://en.wikipedia.org/wiki/Neural_architecture_search Neural Architecture Search | Wikipedia] | * 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. [http://en.wikipedia.org/wiki/Neural_architecture_search Neural Architecture Search | Wikipedia] | ||
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<youtube>sROrvtXnT7Q</youtube> | <youtube>sROrvtXnT7Q</youtube> | ||
Revision as of 20:53, 22 March 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
- NAS with Reinforcement Learning | Wikipedia
- Neural Architecture Search (NAS) with Evolution | Wikipedia
- Multi-objective Neural architecture search | Wikipedia
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
