Difference between revisions of "AI Solver"
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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 |
| − | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools. | + | |
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}} | }} | ||
| + | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]] | ||
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<i>Aids in selecting a starting algorithm for your solution; at that point discover similar algorithms to see which works best for your task (and data) at hand.</i> | <i>Aids in selecting a starting algorithm for your solution; at that point discover similar algorithms to see which works best for your task (and data) at hand.</i> | ||
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Lets get going? I want to... | Lets get going? I want to... | ||
| − | * ...detect patterns... | + | * ...detect patterns or relationships ... [[Causation vs. Correlation|Correlation analysis]] or [[Forecasting|Time series analysis]] |
| − | ** [[...predict values]]/quantity | + | ** [[...predict values]]/quantity/outcomes |
** [[...predict categories]] so I can classify each data point into a specific groups | ** [[...predict categories]] so I can classify each data point into a specific groups | ||
| − | ** [[...cluster]] data points to discover relationships and structure | + | ** [[...cluster]] data points to discover relationships and structure; find hidden structure |
** ...make a [[Recommendation]] | ** ...make a [[Recommendation]] | ||
| − | * ...identify the most important features or perform [[Dimensional Reduction]] | + | * ...identify the most important features (attributes) or perform [[Dimensional Reduction]] |
* [[...find outliers]]; unusual points, anomaly detection | * [[...find outliers]]; unusual points, anomaly detection | ||
| − | * ...find a [[Generative]]-type solution to identify the most plausible theory among competing explanations | + | * ...find a [[Generative AI]]-type solution to identify the most plausible theory among competing explanations |
| − | * ... automate processes; understand (semantic parsing) complete sentences, understanding synonyms of matching words, | + | ** [https://hbr.org/2023/03/a-framework-for-picking-the-right-generative-ai-project A Framework for Picking the Right Generative AI Project | A Framework for Picking the Right Generative AI Project | M. Zao-Sanders & M. Ramos - Harvard Business Review] |
| − | * ... [[Reinforcement Learning (RL) |pathfinding]]; find the best/shortest route to an objective; win a game, traveling salesman problem | + | * ... automate processes; understand (semantic parsing) complete sentences, understanding synonyms of matching words, [[Sentiment Analysis]], or [[Speech Recognition]], (speech) translation ...[[Natural Language Processing (NLP)]] |
| + | * ... [[Reinforcement Learning (RL) |pathfinding]]; learn a series of actions; find the best/shortest route to an objective; win a game, traveling salesman problem ... [[Q Learning]], [[Deep Q Network (DQN)]] | ||
| + | * ... train [[Assistants]], [[Personal Companions]], or [[Agents]] | ||
_____________________________________________________________________________________ | _____________________________________________________________________________________ | ||
| − | |||
* [[Algorithms]] & Neural Network Models to learn about approaches used to solve specific AI-related problems | * [[Algorithms]] & Neural Network Models to learn about approaches used to solve specific AI-related problems | ||
* [[Model Search]] | * [[Model Search]] | ||
| − | * [ | + | * [https://www.mindmeister.com/927441936/machine-learning-algorithms-overview?fullscreen=1 How to pick an algorithm | Willem Meints] |
| − | * [ | + | * [https://www.mindmeister.com/927441936/machine-learning-algorithms-overview?fullscreen=1 Model Mindmap | Mindmeister] |
| + | * [https://www.kdnuggets.com/2020/05/guide-choose-right-machine-learning-algorithm.html An easy guide to choose the right Machine Learning algorithm | Yogita Kinha - KDnuggets] | ||
| + | * [https://dataconomy.com/2023/04/best-ai-models-types-how-to-choose-what-is/ Everything you should know about AI models | Eray Eliaçık - Dataconomy] | ||
| + | |||
| + | |||
| + | https://cdn-images-1.medium.com/max/600/1*iPIGiJIcQjzZheEgTzOnhA.png | ||
| + | |||
| + | == [https://docs.microsoft.com/en-us/azure/machine-learning/studio/algorithm-cheat-sheet Microsoft Azure Studio Cheatsheet] == | ||
| + | *[https://docs.microsoft.com/en-us/azure/machine-learning/studio/algorithm-choice How to choose algorithms for Microsoft Azure Machine Learning | Microsoft] | ||
| + | *[https://docs.microsoft.com/en-us/azure/machine-learning/studio/studio-overview-diagram Overview diagram of Azure Machine Learning Studio capabilities | Microsoft] | ||
| + | * [https://huggingface.co/models Models | Hugging Face] ... click on Sort: Trending | ||
| + | |||
| + | |||
| + | <img src="https://docs.microsoft.com/en-us/azure/machine-learning/studio/media/studio-overview-diagram/ml_studio_overview_v1.1.png" width="1225" height="900"> | ||
| − | |||
| − | + | https://msdnshared.blob.core.windows.net/media/TNBlogsFS/prod.evol.blogs.technet.com/CommunityServer.Blogs.Components.WeblogFiles/00/00/01/02/52/AlgoDecisionTree-2.png | |
| − | |||
| − | |||
| − | + | == [https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html Scikit Machine Learning Map] == | |
| − | |||
| − | = | + | <img src="https://scikit-learn.org/stable/_static/ml_map.png" width="1200" height="850"> |
| − | |||
| − | == [ | + | == [https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/ SAS] == |
| − | + | * [https://www.sas.com/en_us/solutions/ai.html AI] | |
| + | https://blogs.sas.com/content/subconsciousmusings/files/2017/04/machine-learning-cheet-sheet.png | ||
== Notes == | == Notes == | ||
| − | * [ | + | * [https://towardsdatascience.com/notes-on-artificial-intelligence-ai-machine-learning-ml-and-deep-learning-dl-for-56e51a2071c2 Notes on Artificial Intelligence, Machine Learning and Deep Learning for curious people | Özgür Genç - Towards Data Science] |
| − | + | https://cdn-images-1.medium.com/max/800/1*PzeV89iMXPxGMShh6bhwHQ.png | |
| − | + | https://cdn-images-1.medium.com/max/800/1*xlLV8XBECmBTv0dBZKFoyg.png | |
| − | + | https://cdn-images-1.medium.com/max/800/1*qhp867ZtHsO2nPeMdDh4Gw.png | |
| − | + | https://cdn-images-1.medium.com/max/600/1*dgd9vqD96NhUoxUZLMnF_A.png | |
Latest revision as of 07:00, 6 March 2024
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Train, Validate, and Test
Aids in selecting a starting algorithm for your solution; at that point discover similar algorithms to see which works best for your task (and data) at hand.
Lets get going? I want to...
- ...detect patterns or relationships ... Correlation analysis or Time series analysis
- ...predict values/quantity/outcomes
- ...predict categories so I can classify each data point into a specific groups
- ...cluster data points to discover relationships and structure; find hidden structure
- ...make a Recommendation
- ...identify the most important features (attributes) or perform Dimensional Reduction
- ...find outliers; unusual points, anomaly detection
- ...find a Generative AI-type solution to identify the most plausible theory among competing explanations
- ... automate processes; understand (semantic parsing) complete sentences, understanding synonyms of matching words, Sentiment Analysis, or Speech Recognition, (speech) translation ...Natural Language Processing (NLP)
- ... pathfinding; learn a series of actions; find the best/shortest route to an objective; win a game, traveling salesman problem ... Q Learning, Deep Q Network (DQN)
- ... train Assistants, Personal Companions, or Agents
_____________________________________________________________________________________
- Algorithms & Neural Network Models to learn about approaches used to solve specific AI-related problems
- Model Search
- How to pick an algorithm | Willem Meints
- Model Mindmap | Mindmeister
- An easy guide to choose the right Machine Learning algorithm | Yogita Kinha - KDnuggets
- Everything you should know about AI models | Eray Eliaçık - Dataconomy
Microsoft Azure Studio Cheatsheet
- How to choose algorithms for Microsoft Azure Machine Learning | Microsoft
- Overview diagram of Azure Machine Learning Studio capabilities | Microsoft
- Models | Hugging Face ... click on Sort: Trending
Scikit Machine Learning Map
SAS
Notes
