Isomap - Revision history

BPeat at 02:59, 6 March 2024

2024-03-06T02:59:16Z

BPeat at 15:47, 13 September 2023

2023-09-13T15:47:12Z

BPeat at 00:12, 17 August 2023

2023-08-17T00:12:55Z

BPeat at 06:19, 11 July 2023

2023-07-11T06:19:05Z

BPeat at 00:14, 27 June 2023

2023-06-27T00:14:26Z

BPeat: Text replacement - "http:" to "https:"

2023-03-28T00:26:12Z

Text replacement - "http:" to "https:"

BPeat at 01:59, 28 June 2019

2019-06-28T01:59:34Z

BPeat at 15:38, 22 June 2019

2019-06-22T15:38:57Z

BPeat at 04:06, 3 February 2019

2019-02-03T04:06:25Z

BPeat at 22:31, 7 January 2019

2019-01-07T22:31:27Z

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Kernel+Approximation+machine+learning+ML ...Google search]
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 * [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [[Dimensional Reduction]]

@@ Line 18: / Line 18: @@
 * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
-* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [[Dimensional Reduction]]
 * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]

@@ Line 18: / Line 18: @@
 * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
-* [[Embedding]]:  [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [[Dimensional Reduction]]
 * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]

@@ 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
 }}
 [https://www.youtube.com/results?search_query=Kernel+Approximation YouTube search...]
 [https://www.google.com/search?q=Kernel+Approximation+machine+learning+ML ...Google search]
-* [[AI Solver]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
-* [[...find outliers]]
+* [[Embedding]]:  [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
 * [[Dimensional Reduction]]
-* [[Principal Component Analysis (PCA)]]
+* [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]
 ** [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]]
 ** [[Local Linear Embedding (LLE)]]
-* [[Kernel Trick]]
+* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]]
 * [https://en.wikipedia.org/wiki/Isomap Isomap | Wikipedia]
 * [https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction Nonlinear  dimensionality reduction | Wikipedia]

@@ Line 13: / Line 13: @@
 * [[Dimensional Reduction]]
 * [[Principal Component Analysis (PCA)]]
-* [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]]
+* [[Embedding]]
-* [[Local Linear Embedding (LLE)]]
+** [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]]
 * [[Kernel Trick]]
 * [https://en.wikipedia.org/wiki/Isomap Isomap | Wikipedia]
@@ Line 20: / Line 21: @@
 * [https://science.sciencemag.org/content/295/5552/7 The Isomap Algorithm and Topological Stability | M. Balasubramanian, E. Schwartz, J. Tenenbaum, Vin de Silva and J. Langford]
-a nonlinear dimensionality reduction method. It is one of several widely used low-dimensional embedding methods.[1] Isomap is used for computing a quasi-isometric, low-dimensional embedding of a set of high-dimensional data points. The algorithm provides a simple method for estimating the intrinsic geometry of a data manifold based on a rough estimate of each data point’s neighbors on the manifold. Isomap is highly efficient and generally applicable to a broad range of data sources and dimensionalities.
+a nonlinear dimensionality reduction method. It is one of several widely used low-dimensional [[embedding]] methods.[1] Isomap is used for computing a quasi-isometric, low-dimensional [[embedding]] of a set of high-dimensional data points. The algorithm provides a simple method for estimating the intrinsic geometry of a data manifold based on a rough estimate of each data point’s neighbors on the manifold. Isomap is highly efficient and generally applicable to a broad range of data sources and dimensionalities.
 https://science.sciencemag.org/content/sci/295/5552/7/F1.medium.gif

@@ Line 15: / Line 15: @@
 * [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]]
 * [[Local Linear Embedding (LLE)]]
-* [[Kernel Approximation]]
+* [[Kernel Trick]]
 * [http://en.wikipedia.org/wiki/Isomap Isomap | Wikipedia]
 * [http://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction Nonlinear  dimensionality reduction | Wikipedia]

← Older revision		Revision as of 04:06, 3 February 2019
Line 1:		Line 1:
		+	{{#seo:
		+	\|title=PRIMO.ai
		+	\|titlemode=append
		+	\|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
		+	\|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
		+	}}
	[http://www.youtube.com/results?search_query=Kernel+Approximation YouTube search...]		[http://www.youtube.com/results?search_query=Kernel+Approximation YouTube search...]
	[http://www.google.com/search?q=Kernel+Approximation+machine+learning+ML ...Google search]		[http://www.google.com/search?q=Kernel+Approximation+machine+learning+ML ...Google search]