INFORMS的动态

Model Thinking for Everyday Life by Richard C. Larson [Amazon Store] https://amzn.to/4bdrfjt #models #modelthinking #orms #analytics #datascience #data #modeling #informs #AI #ML #optimization #simulation #math #operationsresearch #education

Model Thinking for Everyday Life by Richard C. Larson [Amazon Store] https://amzn.to/4hzE0YE #models #modelthinking #orms #analytics #datascience #data #modeling #informs #AI #ML #optimization #simulation #math #operationsresearch #education

Model Thinking for Everyday Life by Richard C. Larson [Amazon Store] https://amzn.to/3UQZTtw #models #modelthinking #orms #analytics #datascience #data #modeling #informs #AI #ML #optimization #simulation #math #operationsresearch #education

Model Thinking for Everyday Life by Richard C. Larson [Amazon Store] https://hubs.ly/Q02TJpY80 #models #modelthinking #orms #analytics #datascience #data #modeling #informs #AI #ML #optimization #simulation #math #operationsresearch #education

Model Thinking for Everyday Life by Richard C. Larson [Amazon Store] https://hubs.ly/Q02Lmkvy0 #models #modelthinking #orms #analytics #datascience #data #modeling #informs #AI #ML #optimization #simulation #math #operationsresearch #education

?? Excited to share that I've just completed the "Understanding Machine Learning" course! ?? #MachineLearning #ProfessionalDevelopment #DataScience #CareerDevelopment #DataAnalysis #Innovation #Technology #ArtificialIntelligence #BigData #Analytics #SkillDevelopment

Conquer the Maze of Machine Learning Algorithms with this Mind Map Feeling overwhelmed by the vast landscape of machine learning algorithms? You're not alone! But fear not, aspiring data scientists and ML enthusiasts! This comprehensive mind map serves as a valuable guide, visualizing the different categories of machine learning algorithms and their relationships. ? Please feel free to follow me for more content. #machinelearning #datascience #algorithms #datavisualization #mindmap #artificialintelligence #bigdata #datascientist #learning

?? Interested in delving deeper into the mathematical concepts behind machine learning algorithms? ?? Look no further! ?? I have created hand-made notes about the mathematical intuition behind various ML algorithms and techniques. These notes will be regularly posted (bi-weekly) with a focus on the mathematical side of things. ?? The first set of notes are regarding simple linear regression. They can be a helpful resource to those who are also interested in learning machine learning concepts. ?? Check out these hand-written notes and take your machine learning knowledge to the next level! #linearregression #machinelearing #datascience #dataanalytics #statistics

Is there any AI solution that can generate and correctly respond the following Chinese-English multilingual content we wrote manually? 關於 AI 人工智能的一些事實: Some fact findings about AI: https://lnkd.in/gwcPNUPP 其中，用一個簡單的是非題就能顯示我們受著作權保護的中英對照元數據 (metadata) 能做到現今 AI 人工智能無法做到的數據分析工作。 A simple go/no-go test shows that, with our intellectual property (IP), a copyrighted Chinese-English multilingual metadata, we can do what artificial intelligence (AI) can't do in data analytics, NOW.

"Blind trust in predictions breeds doubt, explainable models pave the way out." Couldn't agree more! Consider this scenario: a model that says a bank shouldn't loan someone money, and the bank is legally required to explain the basis for each loan rejection. In this case "Machine Learning Explainability" kicks in. This course by Kaggle sheds light on the inner workings of these powerful tools ? and seeks clarity in their model interpretations. Here's why you should dive in: 1?? Building Trust: Ensure model predictions align with domain-expert intuition, vital in critical fields like medicine. 2?? Debugging: Quickly identify and rectify model inaccuracies when faced with new data. 3?? Informing Feature Engineering: Use model interpretations to enhance feature transformations. 4?? Directing Data Collection: Focus resources on feature collection with high model impact. 5?? Informing Human Decision-Making: Explainable models inform human judgment. Unveiling the Model's Secrets ??: 1?? Permutation Importance: Identify the features with the strongest influence on predictions. (Contribution of each feature) 2?? Partial Dependence Plots: Visualize how individual features affect model outputs. (How does a single feature impact predictions?) 3?? SHAP Values (an acronym from SHapley Additive exPlanations): Show how much a given feature changed our prediction (compared to if we made that prediction at some baseline value of that feature). ?#MachineLearning #Explainability #DataScience #Kaggle #ExplainableAI?#bank #ML #AI #data