Neural Architecture - Revision history

BPeat at 00:07, 9 September 2023

2023-09-09T00:07:20Z

BPeat at 16:36, 17 August 2023

2023-08-17T16:36:29Z

BPeat at 16:35, 17 August 2023

2023-08-17T16:35:49Z

BPeat at 16:07, 17 August 2023

2023-08-17T16:07:02Z

BPeat at 01:03, 13 July 2023

2023-07-13T01:03:44Z

BPeat at 01:01, 13 July 2023

2023-07-13T01:01:34Z

BPeat at 17:59, 2 July 2023

2023-07-02T17:59:14Z

BPeat at 13:33, 10 June 2023

2023-06-10T13:33:51Z

BPeat at 11:08, 8 June 2023

2023-06-08T11:08:09Z

BPeat at 00:59, 14 May 2023

2023-05-14T00:59:50Z

@@ Line 20: / Line 20: @@
 * [[Hierarchical Temporal Memory (HTM)]]
 * [[Codeless Options, Code Generators, Drag n' Drop]]
-* [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
+* [[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]]
 * [[Auto Keras]]
 * [[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]]

@@ Line 47: / Line 47: @@
 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 [[Neural Network|artificial neural networks (ANN]] can be designed and optimized in principle.
+* The search space defines which type of [[Neural Network|artificial neural networks (ANN}]] can be designed and optimized in principle.
 * The search strategy defines which strategy is used to find optimal [[Neural Network|ANN]]'s within the search space.
 * 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]

@@ Line 47: / Line 47: @@
 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 space defines which type of [[Neural Network|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.
+* The search strategy defines which strategy is used to find optimal [[Neural Network|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.  [http://en.wikipedia.org/wiki/Neural_architecture_search Neural Architecture Search | Wikipedia]
+* 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>

@@ Line 47: / Line 47: @@
 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 ANN can be designed and optimized in principle.
+* 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.  [http://en.wikipedia.org/wiki/Neural_architecture_search Neural Architecture Search | Wikipedia]

@@ Line 22: / Line 22: @@
 * [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
 * [[Auto Keras]]
-* [[Evolutionary Computation / Genetic Algorithms]]
+* [[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]]
 * [[Algorithm Administration#Hyperparameter|Hyperparameter]]s Optimization
 * [[Model Search]]

@@ Line 19: / Line 19: @@
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [[Hierarchical Temporal Memory (HTM)]]
 * [[Codeless Options, Code Generators, Drag n' Drop]]
 * [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
@@ Line 27: / Line 26: @@
 * [[Model Search]]
 * [[Google AutoML]]
 = <span id="Neural Architecture Search (NAS)"></span>Neural Architecture Search (NAS) =

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|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
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [http://www.youtube.com/results?search_query=Neural+Architecture+machine+learning YouTube search...]

@@ Line 8: / Line 8: @@
 [http://www.google.com/search?q=Neural+Architecture+machine+learning ...Google search]
-* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ...[[Learning Techniques]]
+* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [[Hierarchical Temporal Memory (HTM)]]
 * [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]