Difference between revisions of "Neural Architecture"

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|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS  
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|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools  
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[http://www.youtube.com/results?search_query=Neural+Architecture+Search+ NAS+machine+learning YouTube search...]
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[http://www.youtube.com/results?search_query=Neural+Architecture+machine+learning YouTube search...]
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[http://www.google.com/search?q=Neural+Architecture+Search+ NAS+machine+learning ...Google search]
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[http://www.google.com/search?q=Neural+Architecture+machine+learning ...Google search]
  
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* [[Automated Machine Learning (AML) - AutoML]]
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* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
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* [[Hyperparameters]] Optimization
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* [[Hierarchical Temporal Memory (HTM)]]
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* [[Other codeless options, Code Generators, Drag n' Drop]]
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* [[Codeless Options, Code Generators, Drag n' Drop]]
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* [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
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* [[Auto Keras]]
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* [[Symbiotic Intelligence]] ... [[Bio-inspired Computing]] ... [[Neuroscience]] ... [[Connecting Brains]] ... [[Nanobots#Brain Interface using AI and Nanobots|Nanobots]] ... [[Molecular Artificial Intelligence (AI)|Molecular]] ... [[Neuromorphic Computing|Neuromorphic]] ... [[Evolutionary Computation / Genetic Algorithms| Evolutionary/Genetic]]
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* [[Algorithm Administration#Hyperparameter|Hyperparameter]]s Optimization
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* [[Model Search]]
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* [[Google AutoML]]
 
* [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]
  
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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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= <span id="Neural Architecture Search (NAS)"></span>Neural Architecture Search (NAS) =
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[http://www.youtube.com/results?search_query=Neural+Architecture+Search+NAS+machine+learning YouTube search...]
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[http://www.google.com/search?q=Neural+Architecture+Search+NAS+machine+learning ...Google search]
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* [http://www.automl.org/automl/literature-on-neural-architecture-search/ Literature on Neural Architecture Search |] [http://www.automl.org/ AutoML.org]
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* [http://github.com/D-X-Y/awesome-NAS Awesome NAS]; a curated list
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* [http://en.wikipedia.org/wiki/Neural_architecture_search#NAS_with_Reinforcement_Learning Neural Architecture Search (NAS) with Reinforcement Learning | Wikipedia]
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** [[Reinforcement Learning (RL)]]
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* [http://en.wikipedia.org/wiki/Neural_architecture_search#NAS_with_Evolution Neural Architecture Search (NAS) with Evolution | Wikipedia]
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* [http://en.wikipedia.org/wiki/Neural_architecture_search#Multi-objective_Neural_architecture_search Multi-objective Neural architecture search | Wikipedia]
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* [http://arxiv.org/abs/1904.09523 Neural Architecture Search for Deep Face Recognition | Ning Zhu]
  
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* The search space defines which type of ANN can be designed and optimized in principle.
 
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* The search strategy defines which strategy is used to find optimal ANN's within the search space.
 
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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]
 
  
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* [http://en.wikipedia.org/wiki/Neural_architecture_search#NAS_with_Reinforcement_Learning NAS with Reinforcement Learning | Wikipedia]
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An alternative to manual design is “neural architecture search” (NAS), a series of machine learning techniques that can help discover optimal neural networks for a given problem. Neural architecture search is a big area of research and holds a lot of promise for future applications of deep learning.  * [http://thenextweb.com/news/best-ai-model-for-your-problem-try-neural-architecture-search Need to find the best AI model for your problem? Try neural architecture search | Ben Dickson - TDW] NAS algorithms are efficient problem solvers  ... [http://bdtechtalks.com/2022/02/28/what-is-neural-architecture-search/ What is neural architecture search (NAS)? | Ben Dickson - TechTalks]
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http://cdn0.tnwcdn.com/wp-content/blogs.dir/1/files/2022/03/deep.jpg
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Various approaches to Neural Architecture Search (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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* The search space defines which type of [[Neural Network|artificial neural networks (ANN}]] can be designed and optimized in principle.
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* The search strategy defines which strategy is used to find optimal [[Neural Network|ANN]]'s within the search space.
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* Obtaining the performance of an [[Neural Network|ANN]] is costly as this requires training the [[Neural Network|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]
  
 
<youtube>sROrvtXnT7Q</youtube>
 
<youtube>sROrvtXnT7Q</youtube>
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<youtube>CYUpDogeIL0</youtube>
 
<youtube>CYUpDogeIL0</youtube>
 
<youtube>MtuADH7kVeQ</youtube>
 
<youtube>MtuADH7kVeQ</youtube>
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<youtube>BAtBwgV1fG0</youtube>
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<youtube>5ke9ZEvXJEk</youtube>
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= Differentiable Neural Computer (DNC) =
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[[http://www.youtube.com/results?search_query=differentiable+neural+computers+%28DNC%29 YouTube search...]]
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<youtube>r5XKzjTFCZQ</youtube>
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= <span id="Neural Operator"></span>Neural Operator =
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[http://www.youtube.com/results?search_query=Neural+Operator+machine+learning+AI YouTube search...]
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[http://www.google.com/search?q=Neural+Operator+machine+learning+AI  ...Google search]
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* [[Geology: Mining, Oil & Gas]]
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* [https://arxiv.org/abs/2108.08481 Neural Operator: Learning Maps Between Function Spaces | N. Kovachki, Z. Li, B. Liu, K. Azizzadenesheli, K. Bhattacharya, A. Stuart,] [[Creatives#Animashree (Anima) Anandkumar | Animashree (Anima) Anandkumar]]
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* [https://www.ai4science.caltech.edu/projects/neural-operator.html Neural Operator – Solving PDEs; Partial Differential Equations | ][[Creatives#Animashree (Anima) Anandkumar | Animashree (Anima) Anandkumar]], Andrew Stuart, & Kaushik Bhattacharya]
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A generalization of neural networks to learn operators, termed neural operators, that map between infinite dimensional function spaces. A universal approximator in the function space. the Fourier neural operator model has shown state-of-the-art performance with 1000x speedup in learning turbulent Navier-Stokes equation, as well as promising applications in weather forecast and CO2 migration, as shown in the figure below. [https://zongyi-li.github.io/neural-operator/ Neural Operator Machine learning for scientific computing | Zongy Li]
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https://zongyi-li.github.io/neural-operator/img/FNO-demo.gif
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<img src="https://zongyi-li.github.io/neural-operator/img/fourier_full_arch5.png" width="800">
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<youtube>Bd4KvlmGbY4</youtube>
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<youtube>KIGG-IA9awU</youtube>
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<youtube>IaS72aHrJKE</youtube>
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<youtube>0Ve9xwNJO2o</youtube>
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<youtube>7AnW8b6hfFI</youtube>
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<youtube>UMo4mSc42Cc</youtube>

Latest revision as of 20:07, 8 September 2023

YouTube search... ...Google search

Neural Architecture Search (NAS)

YouTube search... ...Google search


An alternative to manual design is “neural architecture search” (NAS), a series of machine learning techniques that can help discover optimal neural networks for a given problem. Neural architecture search is a big area of research and holds a lot of promise for future applications of deep learning. * Need to find the best AI model for your problem? Try neural architecture search | Ben Dickson - TDW NAS algorithms are efficient problem solvers ... What is neural architecture search (NAS)? | Ben Dickson - TechTalks

deep.jpg

Various approaches to Neural Architecture Search (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 artificial neural networks (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

Differentiable Neural Computer (DNC)

[YouTube search...]

Neural Operator

YouTube search... ...Google search


A generalization of neural networks to learn operators, termed neural operators, that map between infinite dimensional function spaces. A universal approximator in the function space. the Fourier neural operator model has shown state-of-the-art performance with 1000x speedup in learning turbulent Navier-Stokes equation, as well as promising applications in weather forecast and CO2 migration, as shown in the figure below. Neural Operator Machine learning for scientific computing | Zongy Li

FNO-demo.gif

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