Difference between revisions of "...find outliers"
m |
m |
||
| (7 intermediate revisions by the same user not shown) | |||
| Line 2: | Line 2: | ||
|title=PRIMO.ai | |title=PRIMO.ai | ||
|titlemode=append | |titlemode=append | ||
| − | |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 |
| − | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | + | |
| + | <!-- Google tag (gtag.js) --> | ||
| + | <script async src="https://www.googletagmanager.com/gtag/js?id=G-4GCWLBVJ7T"></script> | ||
| + | <script> | ||
| + | window.dataLayer = window.dataLayer || []; | ||
| + | function gtag(){dataLayer.push(arguments);} | ||
| + | gtag('js', new Date()); | ||
| + | |||
| + | gtag('config', 'G-4GCWLBVJ7T'); | ||
| + | </script> | ||
}} | }} | ||
[https://www.youtube.com/results?search_query=~outlier+~diversity+AI YouTube] | [https://www.youtube.com/results?search_query=~outlier+~diversity+AI YouTube] | ||
| Line 11: | Line 20: | ||
[https://www.bing.com/news/search?q=~outlier+~diversity+AI&qft=interval%3d%228%22 ...Bing News] | [https://www.bing.com/news/search?q=~outlier+~diversity+AI&qft=interval%3d%228%22 ...Bing News] | ||
| + | * [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]]. [[...find outliers]] | ||
* [[AI Solver]] | * [[AI Solver]] | ||
** Looking for event based? | ** Looking for event based? | ||
| Line 27: | Line 37: | ||
* [https://medium.com/@srnghn/machine-learning-trying-to-detect-outliers-or-unusual-behavior-2d9f364334f9 Machine Learning: Trying to detect outliers or unusual behavior | Stacey Ronaghan - Medium] | * [https://medium.com/@srnghn/machine-learning-trying-to-detect-outliers-or-unusual-behavior-2d9f364334f9 Machine Learning: Trying to detect outliers or unusual behavior | Stacey Ronaghan - Medium] | ||
* [https://pdfs.semanticscholar.org/4c68/4a9ba057fb7e61733ff554fe2975a2c91096.pdf Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering | A. Barai and L. Dey] | * [https://pdfs.semanticscholar.org/4c68/4a9ba057fb7e61733ff554fe2975a2c91096.pdf Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering | A. Barai and L. Dey] | ||
| − | * [[ | + | * [[Cybersecurity]] ... [[Open-Source Intelligence - OSINT |OSINT]] ... [[Cybersecurity Frameworks, Architectures & Roadmaps | Frameworks]] ... [[Cybersecurity References|References]] ... [[Offense - Adversarial Threats/Attacks| Offense]] ... [[National Institute of Standards and Technology (NIST)|NIST]] ... [[U.S. Department of Homeland Security (DHS)| DHS]] ... [[Screening; Passenger, Luggage, & Cargo|Screening]] ... [[Law Enforcement]] ... [[Government Services|Government]] ... [[Defense]] ... [[Joint Capabilities Integration and Development System (JCIDS)#Cybersecurity & Acquisition Lifecycle Integration| Lifecycle Integration]] ... [[Cybersecurity Companies/Products|Products]] ... [[Cybersecurity: Evaluating & Selling|Evaluating]] |
| − | + | * [[Signal Processing]] | |
| − | + | * [[Pathology]] | |
| − | |||
| − | |||
| − | |||
<hr><center> | <hr><center> | ||
| − | <b>Outliers | + | <b>Outliers don't necessarily represent abnormal behavior |
| − | </center> | + | </b></center> |
<hr> | <hr> | ||
Latest revision as of 04:36, 13 September 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- Embedding ... Fine-tuning ... RAG ... Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction. ...find outliers
- AI Solver
- Looking for event based?
- Do you have > 100 features?
- Yes, then try One-class Support Vector Machine (SVM)
- No, need fast training, then try Principal Component Analysis (PCA)-based Anomaly Detection
- Also consider...
- Outlier | Wikipedia
- Machine Learning: Trying to detect outliers or unusual behavior | Stacey Ronaghan - Medium
- Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering | A. Barai and L. Dey
- Cybersecurity ... OSINT ... Frameworks ... References ... Offense ... NIST ... DHS ... Screening ... Law Enforcement ... Government ... Defense ... Lifecycle Integration ... Products ... Evaluating
- Signal Processing
- Pathology
Outliers don't necessarily represent abnormal behavior
An Outlier is a rare chance of occurrence within a given data set. In Data Science, an Outlier is an observation point that is distant from other observations. An Outlier may be due to variability in the measurement or it may indicate experimental error. Outliers, being the most extreme observations, may include the sample maximum or sample minimum, or both, depending on whether they are extremely high or low. However, the sample maximum and minimum are not always outliers because they may not be unusually far from other observations. - Outlier Detection and Anomaly Detection with Machine Learning | Mehul Ved - Medium
