Difference between revisions of "Checklists"

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|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Meta, Facebook, Azure, Amazon, AWS  
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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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|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=how+checklist+artificial+intelligence+deep+machine+learning+models YouTube search...]
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[https://www.youtube.com/results?search_query=ai+checklist YouTube search...]
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[https://www.google.com/search?q=how+checklist+artificial+intelligence+deep+machine+learning+models ...Google search]
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[https://www.quora.com/search?q=ai%20checklist ... Quora search]
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[https://www.google.com/search?q=ai+checklist ...Google search]
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[https://news.google.com/search?q=ai+checklist ...Google News]
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[https://www.bing.com/news/search?q=ai+checklist&qft=interval%3d%228%22 ...Bing News]
  
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* [[AI Solver]]
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* [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
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* [[Strategy & Tactics]] ...[[Strategy & Tactics#Business Case|business case]]
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* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
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* [[Evaluation]]
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* [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
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** [[Evaluation - Measures]]  
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* [[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]]
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* [[AI Governance]]
 
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** [[Data Governance]]
 
 
* [[Case Studies]]
 
* [[Case Studies]]
 
** [[Hospitality, Food, and Spirits]]
 
** [[Hospitality, Food, and Spirits]]
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** [https://pair.withgoogle.com/guidebook/patterns/how-do-i-get-started Patterns]
 
** [https://pair.withgoogle.com/guidebook/patterns/how-do-i-get-started Patterns]
 
* [https://www.jennwv.com/papers/checklists.pdf Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI | M. Madaio, L. Stark, J. Vaughan, and H. Wallach]  
 
* [https://www.jennwv.com/papers/checklists.pdf Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI | M. Madaio, L. Stark, J. Vaughan, and H. Wallach]  
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<youtube>LqLCZshLgVM</youtube>
 
<youtube>LqLCZshLgVM</youtube>
 
<b>Common mistakes made in Machine Learning Models
 
<b>Common mistakes made in Machine Learning Models
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</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.  
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</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.  
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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/
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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/
 
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Latest revision as of 21:44, 5 March 2024

YouTube search... ... Quora search ...Google search ...Google News ...Bing News


Lists...


Common Mistakes

YouTube search... ...Google search


Common mistakes made in Machine Learning Models
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/

Cameron Davidson Pilon: Mistakes I've Made
PyData Seattle 2015 In this humbling talk, I'll describe some mistakes I've made in working in statistics and machine learning. I'll describe my original intentions, symptoms, how I eventually discovered the mistake, and possibly even a solution. The topics include mistakes in A/B testing, Kaggle competitions, data collection, and other fields. In this humbling talk, I'll describe some mistakes I've made in working in statistics and machine learning. I'll describe my original intentions, symptoms, how I eventually discovered the mistake, and possibly even a solution. The topics include mistakes in A/B testing, Kaggle competitions, data collection, and other fields. I'll also introduce some interesting statistical and machine learning counterexamples: examples where our original intuition fails, and solutions to these examples.

AI Simplified: Top 3 Rookie Mistakes in Machine Learning
John Boersma, Director of Education at DataRobot, shares his list of the top three rookie mistakes in machine learning for our AI Simplified series. Learn more about simplified AI terms on our wiki page: https://www.datarobot.com/wiki

Top 10 Machine Learning Pitfalls – Mark Landry
Over-fitting, misread data, NAs, collinear column elimination and other common issues play havoc in the day of practicing data scientist. In this talk, Mark Landry, one of the world’s leading Kagglers, will review the top 10 common pitfalls and steps to avoid them. View more talks from H2O Open Tour Dallas https://open.h2o.ai/dallas.html Powered by the open source machine learning software H2O.ai. Contributors welcome at https://github.com/h2oai To access slides on H2O open source machine learning software, go to: https://www.slideshare.net/0xdata

AI Failures

"Machine learning failures - for art!" by Janelle Shane
It's tough to write a machine learning algorithm that works well. Overfitting, noisy data, a problem that's too general - these problems plague the programmers who apply these algorithms to financial modeling and image labeling. But mistakes can also be fun. At her humor blog AIweirdness.com, Janelle Shane posts examples of machine learning algorithms going terribly, hilariously wrong. Here, she talks about some common machine learning mistakes - and how to use them deliberately.

Lessons Learned from Machine Learning Gone Wrong - Janelle Shane
It's tough to write a machine learning algorithm that works well. Overfitting, noisy data, a problem that's too general - these problems plague the programmers who apply these algorithms to financial modeling and image labeling. But mistakes can also be fun. At her humor blog AIweirdness.com, Janelle Shane posts examples of machine learning algorithms going terribly, hilariously wrong. Here, she talks about some common machine learning mistakes - and how to use them deliberately. About Janelle: Janelle Shane trains neural networks, a type of machine learning algorithm, to write unintentional humor as they struggle to imitate human datasets. Well, she intends the humor. The neural networks are just doing their best to understand what's going on. Currently located on the occupied land of the Arapahoe Nation.

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