Difference between revisions of "Checklists"
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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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| − | [ | + | [https://www.youtube.com/results?search_query=ai+checklist YouTube search...] |
| − | [ | + | [https://www.quora.com/search?q=ai%20checklist ... Quora search] |
| + | [https://www.google.com/search?q=ai+checklist ...Google search] | ||
| + | [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] | ||
| − | * [[ | + | * [[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]] ... [[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]] | |
| − | * [[AI Governance]] | ||
| − | * | ||
* [[Case Studies]] | * [[Case Studies]] | ||
** [[Hospitality, Food, and Spirits]] | ** [[Hospitality, Food, and Spirits]] | ||
*** [[Recipes]] | *** [[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] | |
| + | * [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] | ||
=== Lists... === | === Lists... === | ||
| + | * [https://towardsdatascience.com/the-essential-machine-learning-project-checklist-3ad6a7a49c37 The Essential Machine Learning Project Checklist | ] [https://PatrickDeGuzman.me |Patrick De Guzman] - Towards Data Science ...To guide you step-by-step from raw data to a working ML model. | ||
| + | * [https://medium.com/@subhojit20_27731/ml-project-checklist-document-for-my-own-reference-feel-free-to-use-it-if-you-want-reference-408b4b6a8fb9 ML project checklist | Subhojit Banerjee - Medium] | ||
| + | * [https://www.kdnuggets.com/2018/12/machine-learning-project-checklist.html The Machine Learning Project Checklist | Matthew Mayo] | ||
| + | * [https://towardsdatascience.com/a-checklist-for-working-with-complex-ml-problems-3ea729362db0 A Checklist for working with Complex ML Problems | Sanchit Aggarwal] | ||
| + | * [https://www.fast.ai/2020/01/07/data-questionnaire/ Data project checklist | fast.ai] | ||
| + | * [https://www.acq-intl.com/5-questions-to-ask-before-putting-ai-into-practice-and-a-checklist-for-success/ 5 Questions To Ask Before Putting AI Into Practice And A Checklist For Success | Acquisition International] | ||
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| − | + | * [https://roboticsbiz.com/machine-learning-cheat-sheets-compilation-2020/ Machine Learning cheat sheets – Compilation 2020 | RoboticsBiz] | |
| − | * [ | + | * [https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463 Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data | Stefan Kojouharov - BecomingHuman.AI - Medium] ...[https://github.com/kailashahirwar/cheatsheets-ai GitHub] ...[https://dl.icdst.org/pdfs/files3/b92e3bad40a8e52c3a34d152c266b235.pdf PDF] |
| − | * [ | + | * [https://medium.com/predict/the-complete-list-to-make-you-an-ai-pro-be83448720b8 The complete list to make you an AI Pro | Rita Dass - Predict - Medium] |
| − | * [ | + | * [https://machinelearningmastery.com/machine-learning-checklist/ How to Use a Machine Learning Checklist to Get Accurate Predictions, Reliably (even if you are a beginner) | Jason Brownlee] |
| − | * [ | + | * [https://www.ic.unicamp.br/~sandra/pdf/Hands_On_Machine_Learning_with_Scikit_Learn_and_TensorFlow-427-432.pdf Machine Learning project checklist | IC-Unicamp] |
| − | * [ | + | * [https://www.datanami.com/2017/09/01/machine-learning-ready-7-part-checklist/ Machine Learning: Are You Ready? A 7-Part Checklist | Kimberly Nevala] |
| − | * [ | + | * [https://ai.facebook.com/blog/new-code-completeness-checklist-and-reproducibility-updates/ New code completeness checklist and reproducibility updates |] [[Meta|Facebook]] AI |
| − | * [ | + | * [https://www.jeremyjordan.me/ml-projects-guide/ Organizing machine learning projects: project management guidelines | Jeremy Jordan] |
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= <span id="Common Mistakes"></span>Common Mistakes = | = <span id="Common Mistakes"></span>Common Mistakes = | ||
| − | [ | + | [https://www.youtube.com/results?search_query=mistakes+avoid+pitfall+checklist+artificial+intelligence+deep+Machine+learning+models YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=mistakes+avoid+pitfall+checklist+artificial+intelligence+deep+Machine+learning+models ...Google search] |
| − | * [ | + | * [https://www.computerworld.co.nz/article/521906/12_predictive_analytics_screw-ups/ 12 predictive analytics screw-ups | Robert L. Mitchell] |
| − | * [ | + | * [https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0155-3 Ten quick tips for machine learning in computational biology | Davide Chicco] |
| − | * [ | + | * [https://www.kdnuggets.com/2017/10/top-errors-novice-machine-learning-engineers.html Top 6 errors novice machine learning engineers make | Christopher Dossman] |
| − | * [ | + | * [https://www.sas.com/en_us/insights/articles/big-data/5-machine-learning-mistakes.html 5 machine learning mistakes – and how to avoid them | SAS] |
| − | * [ | + | * [https://www.analyticsvidhya.com/blog/2018/07/13-common-mistakes-aspiring-fresher-data-scientists-make-how-to-avoid-them/ 13 Common Mistakes Amateur Data Scientists Make and How to Avoid Them? | Pranav Dar - Analytics Vidhya] |
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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 | ||
| − | </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 : | + | 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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<youtube>uvAyXmICUNM</youtube> | <youtube>uvAyXmICUNM</youtube> | ||
<b>AI Simplified: Top 3 Rookie Mistakes in Machine Learning | <b>AI Simplified: Top 3 Rookie Mistakes in Machine Learning | ||
| − | </b><br>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: | + | </b><br>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 |
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<youtube>_JnERKNat4w</youtube> | <youtube>_JnERKNat4w</youtube> | ||
<b>Top 10 Machine Learning Pitfalls – Mark Landry | <b>Top 10 Machine Learning Pitfalls – Mark Landry | ||
| − | </b><br>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 | + | </b><br>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 |
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Latest revision as of 21:44, 5 March 2024
YouTube search... ... Quora search ...Google search ...Google News ...Bing News
- Strategy & Tactics ... Project Management ... Best Practices ... Checklists ... Project Check-in ... Evaluation ... Measures
- AI Solver ... Algorithms ... 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 ... Governance ... Preprocessing ... Exploration ... Interoperability ... Master Data Management (MDM) ... Bias and Variances ... Benchmarks ... Datasets
- Case Studies
- ML Design Guides | Google ... People + AI Guidebook is a set of methods, best practices and examples for designing with AI
- Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI | M. Madaio, L. Stark, J. Vaughan, and H. Wallach
Lists...
- The Essential Machine Learning Project Checklist | |Patrick De Guzman - Towards Data Science ...To guide you step-by-step from raw data to a working ML model.
- ML project checklist | Subhojit Banerjee - Medium
- The Machine Learning Project Checklist | Matthew Mayo
- A Checklist for working with Complex ML Problems | Sanchit Aggarwal
- Data project checklist | fast.ai
- 5 Questions To Ask Before Putting AI Into Practice And A Checklist For Success | Acquisition International
- Machine Learning cheat sheets – Compilation 2020 | RoboticsBiz
- Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data | Stefan Kojouharov - BecomingHuman.AI - Medium ...GitHub ...PDF
- The complete list to make you an AI Pro | Rita Dass - Predict - Medium
- How to Use a Machine Learning Checklist to Get Accurate Predictions, Reliably (even if you are a beginner) | Jason Brownlee
- Machine Learning project checklist | IC-Unicamp
- Machine Learning: Are You Ready? A 7-Part Checklist | Kimberly Nevala
- New code completeness checklist and reproducibility updates | Facebook AI
- Organizing machine learning projects: project management guidelines | Jeremy Jordan
Common Mistakes
YouTube search... ...Google search
- 12 predictive analytics screw-ups | Robert L. Mitchell
- Ten quick tips for machine learning in computational biology | Davide Chicco
- Top 6 errors novice machine learning engineers make | Christopher Dossman
- 5 machine learning mistakes – and how to avoid them | SAS
- 13 Common Mistakes Amateur Data Scientists Make and How to Avoid Them? | Pranav Dar - Analytics Vidhya
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