Checklists - Revision history

BPeat at 02:44, 6 March 2024

2024-03-06T02:44:03Z

2023-07-11T02:00:23Z

2023-07-09T15:23:47Z

2023-07-09T12:55:59Z

2023-07-07T02:41:01Z

2023-06-30T16:29:27Z

2023-03-19T16:58:28Z

2023-03-19T11:53:54Z

2023-03-19T03:20:19Z

2023-03-05T12:23:34Z

@@ Line 21: / Line 21: @@
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
-* [[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]]
 * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
 * [[Data Science]] ... [[Data Governance|Governance]] ... [[Data Preprocessing|Preprocessing]] ... [[Feature Exploration/Learning|Exploration]] ... [[Data Interoperability|Interoperability]] ... [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] ... [[Bias and Variances]] ... [[Benchmarks]] ... [[Datasets]]

@@ Line 21: / Line 21: @@
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
-* [[AI Solver]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
 * [[Data Science]] ... [[Data Governance|Governance]] ... [[Data Preprocessing|Preprocessing]] ... [[Feature Exploration/Learning|Exploration]] ... [[Data Interoperability|Interoperability]] ... [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] ... [[Bias and Variances]] ... [[Benchmarks]] ... [[Datasets]]

@@ Line 20: / Line 20: @@
 [https://www.bing.com/news/search?q=ai+checklist&qft=interval%3d%228%22 ...Bing News]
 * [[AI Solver]]
 * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
 * [[Data Science]] ... [[Data Governance|Governance]] ... [[Data Preprocessing|Preprocessing]] ... [[Feature Exploration/Learning|Exploration]] ... [[Data Interoperability|Interoperability]] ... [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] ... [[Bias and Variances]] ... [[Benchmarks]] ... [[Datasets]]

@@ Line 68: / Line 68: @@
 <youtube>LqLCZshLgVM</youtube>
 <b>Common mistakes made in Machine Learning Models
-</b><br>Analytics University  You will learn  the common mistake people make while building machine learning models.  Machine learning models are easy to build but need attention to details.
+</b><br>[[Analytics]] University  You will learn  the common mistake people make while building machine learning models.  Machine learning models are easy to build but need attention to details.
-The common mistakes could be: 1- taking Default Loss Function for granted, 2- Using one Algorithm / Method  For All  Problems: 3- Ignoring Outliers: 4- No Proper Dealing With Cyclical Features, 5- L1/L2 Regularisation Without Standardization, 6- Interpreting Coefficients From Linear or Logistic Regressions as features importance. Analytics Study Pack : https://analyticuniversity.com/
+The common mistakes could be: 1- taking Default Loss Function for granted, 2- Using one Algorithm / Method  For All  Problems: 3- Ignoring Outliers: 4- No Proper Dealing With Cyclical Features, 5- L1/L2 Regularisation Without Standardization, 6- Interpreting Coefficients From Linear or Logistic Regressions as features importance. [[Analytics]] Study Pack : https://analyticuniversity.com/
 |}
 |<!-- M -->

@@ Line 22: / Line 22: @@
 * [[AI Solver]]
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
-* [[AI Governance]]
+* [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
-** [[Data Governance]]
+* [[Data Science]] ... [[Data Governance|Governance]] ... [[Data Preprocessing|Preprocessing]] ... [[Feature Exploration/Learning|Exploration]] ... [[Data Interoperability|Interoperability]] ... [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] ... [[Bias and Variances]] ... [[Benchmarks]] ... [[Datasets]]
 * [[Case Studies]]
 ** [[Hospitality, Food, and Spirits]]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Meta, Facebook, 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=ai+checklist YouTube search...]
@@ Line 10: / Line 19: @@
 [https://news.google.com/search?q=ai+checklist ...Google News]
 [https://www.bing.com/news/search?q=ai+checklist&qft=interval%3d%228%22 ...Bing News]
 * [[AI Solver]]

@@ Line 12: / Line 12: @@
 * [[AI Solver]]
-* [[Strategy & Tactics]]  ...[[Strategy & Tactics#Business Case|business case]]
+* [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
 * [[AI Governance]]
 ** [[Data Governance]]
@@ Line 20: / Line 18: @@
 ** [[Hospitality, Food, and Spirits]]
 *** [[Recipes]]
 * [https://pair.withgoogle.com/guidebook ML Design Guides | ][[Google]] ... People + AI Guidebook is a set of methods, best practices and examples for designing with AI
 ** [https://pair.withgoogle.com/guidebook/patterns/how-do-i-get-started Patterns]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[https://www.youtube.com/results?search_query=how+checklist+artificial+intelligence+deep+machine+learning+models YouTube search...]
+[https://www.youtube.com/results?search_query=ai+checklist YouTube search...]
-[https://www.google.com/search?q=how+checklist+artificial+intelligence+deep+machine+learning+models ...Google search]
+[https://www.quora.com/search?q=ai%20checklist ... Quora search]
 * [[AI Solver]]