Difference between revisions of "K-Nearest Neighbors (KNN)"
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| − | |keywords=artificial, intelligence, machine, learning, models | + | |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=ai+Nearest+Neighbors+KNN YouTube] |
| − | [ | + | [https://www.quora.com/search?q=ai%20Nearest%20Neighbors%20KNN ... Quora] |
| + | [https://www.google.com/search?q=ai+Nearest+Neighbors+KNN ... Google search] | ||
| + | [https://news.google.com/search?q=ai+Nearest+Neighbors+KNN ... Google News] | ||
| + | [https://www.bing.com/news/search?q=ai+Nearest+Neighbors+KNN&qft=interval%3d%228%22 ... Bing News] | ||
| − | * [[AI Solver]] | + | * [[Approximate Nearest Neighbor (ANN)]] |
| + | * [[Hierarchical Navigable Small World (HNSW)]] | ||
| + | * [[Inverted File Indexes (IVF)]] | ||
| + | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]] | ||
** [[...predict categories]] | ** [[...predict categories]] | ||
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| − | This is a simple algorithm which predicts unknown data point with its k nearest neighbors. The value of k is a critical factor here regarding the accuracy of prediction. It determines the nearest by calculating the distance using basic distance functions like Euclidean. However, this algorithm needs high computation power and we need to normalize data initially to bring every data point to same range. [ | + | * [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]] |
| + | * [https://www.unite.ai/what-is-k-nearest-neighbors/ What Is K-Nearest Neighbors? | Daniel Nelson - Unite.ai] | ||
| + | |||
| + | Can be used for regression or classification by choosing 'K'; where 'K' = # of closest neighboring data points that the model uses in prediction. | ||
| + | |||
| + | This is a simple algorithm which predicts unknown data point with its k nearest neighbors. The value of k is a critical factor here regarding the accuracy of prediction. It determines the nearest by calculating the distance using basic distance functions like Euclidean. However, this algorithm needs high computation power and we need to normalize data initially to bring every data point to same range. [https://towardsdatascience.com/10-machine-learning-algorithms-you-need-to-know-77fb0055fe0 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium] | ||
| − | + | https://cdn-images-1.medium.com/max/800/1*9h-WczF6csbeut5vsvjSUA.gif | |
<youtube>MDniRwXizWo</youtube> | <youtube>MDniRwXizWo</youtube> | ||
Latest revision as of 22:54, 5 March 2024
YouTube ... Quora ... Google search ... Google News ... Bing News
- Approximate Nearest Neighbor (ANN)
- Hierarchical Navigable Small World (HNSW)
- Inverted File Indexes (IVF)
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Train, Validate, and Test
- Excel ... Documents ... Database; Vector & Relational ... Graph ... LlamaIndex
- What Is K-Nearest Neighbors? | Daniel Nelson - Unite.ai
Can be used for regression or classification by choosing 'K'; where 'K' = # of closest neighboring data points that the model uses in prediction.
This is a simple algorithm which predicts unknown data point with its k nearest neighbors. The value of k is a critical factor here regarding the accuracy of prediction. It determines the nearest by calculating the distance using basic distance functions like Euclidean. However, this algorithm needs high computation power and we need to normalize data initially to bring every data point to same range. 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium
