Difference between revisions of "Isomap"
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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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| + | [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]] ... [[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]] | ||
| + | * [[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)]] | ||
| + | * [[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] | ||
| + | * [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] | ||
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| + | 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 | ||
Latest revision as of 22:59, 5 March 2024
YouTube search... ...Google search
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Train, Validate, and Test
- Embedding ... Fine-tuning ... RAG ... Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction. ...find outliers
- Dimensional Reduction
- Backpropagation ... FFNN ... Forward-Forward ... Activation Functions ...Softmax ... Loss ... Boosting ... Gradient Descent ... Hyperparameter ... Manifold Hypothesis ... PCA
- Math for Intelligence ... Finding Paul Revere ... Social Network Analysis (SNA) ... Dot Product ... Kernel Trick
- Isomap | Wikipedia
- Nonlinear dimensionality reduction | Wikipedia
- 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.
