001482407 000__ 05537cam\\22006017a\4500
001482407 001__ 1482407
001482407 003__ OCoLC
001482407 005__ 20231128003335.0
001482407 006__ m\\\\\o\\d\\\\\\\\
001482407 007__ cr\un\nnnunnun
001482407 008__ 231014s2023\\\\si\\\\\\ob\\\\000\0\eng\d
001482407 019__ $$a1402735622
001482407 020__ $$a9789819963539$$q(electronic bk.)
001482407 020__ $$a9819963532$$q(electronic bk.)
001482407 020__ $$z9819963524
001482407 020__ $$z9789819963522
001482407 0247_ $$a10.1007/978-981-99-6353-9$$2doi
001482407 035__ $$aSP(OCoLC)1402819592
001482407 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dOCLCF
001482407 049__ $$aISEA
001482407 050_4 $$aQ335$$b.M34 2023
001482407 08204 $$a006.3$$223/eng/20231023
001482407 1001_ $$aMahalle, Parikshit N.
001482407 24510 $$aData centric artificial intelligence :$$ba beginner's guide /$$cParikshit N. Mahalle, Gitanjali R. Shinde, Yashwant S. Ingle, Namrata N. Wasatkar.
001482407 260__ $$aSingapore :$$bSpringer,$$c2023.
001482407 300__ $$a1 online resource (137 p.).
001482407 4901_ $$aData-Intensive Research
001482407 500__ $$a8.3 Application Implementation in Data-Centric Approach
001482407 504__ $$aIncludes bibliographical references.
001482407 5050_ $$aIntro -- Preface -- Contents -- About the Authors -- 1 Introduction -- 1.1 Building Blocks of AI -- 1.2 AI Current State -- 1.3 Motivation -- 1.4 Need for Paradigm Shift from Model-Centric AI to Data-Centric AI -- 1.5 Summary -- References -- 2 Model-Centric AI -- 2.1 Working Principle -- 2.1.1 Supervised Learning -- 2.1.2 Unsupervised Learning -- 2.1.3 Reinforcement Learning -- 2.2 Learning Methods -- 2.2.1 Supervised Machine Learning Algorithms -- 2.2.2 Unsupervised Machine Learning Algorithms -- 2.2.3 Deep Learning Algorithms -- 2.3 Model Building -- 2.4 Model Training -- 2.5 Model Testing
001482407 5058_ $$a2.6 Model Tuning -- 2.7 Use Cases: Model-Centric AI -- 2.8 Summary -- References -- 3 Data-Centric Principles for AI Engineering -- 3.1 Overview -- 3.2 AI Engineering -- 3.3 Challenges -- 3.4 Data-Centric Principles -- 3.5 Summary -- References -- 4 Mathematical Foundation for Data-Centric AI -- 4.1 Overview -- 4.1.1 Statistics -- 4.1.2 Linear Algebra -- 4.1.3 Calculus -- 4.1.4 Probability Theory -- 4.1.5 Multivariate Calculus -- 4.1.6 Graph Theory -- 4.2 Statistical Data Analysis -- 4.3 Data Tendency and Distribution -- 4.3.1 Data Tendency/Measure of Central Tendency
001482407 5058_ $$a4.3.2 Measure of Dispersion -- 4.3.3 Data Distribution -- 4.4 Data Models -- 4.5 Optimization Techniques -- 4.6 Summary -- References -- 5 Data-Centric AI -- 5.1 Data Acquisition -- 5.1.1 The Data Acquisition Process -- 5.1.2 Key Insights for Big Data Acquisition -- 5.1.3 Case Study: Data Acquisition for Retail Company -- 5.2 Data Labeling -- 5.2.1 How Does Data Labeling Work? -- 5.2.2 Data Labeling Approaches -- 5.2.3 Importance of Data Labeling -- 5.2.4 Case Study: Data Labeling for Autonomous Vehicle Training -- 5.3 Data Annotation -- 5.3.1 Types of Data Annotation
001482407 5058_ $$a5.3.2 Case Study on Data Annotation -- 5.4 Data Augmentation -- 5.4.1 How Does Data Augmentation Work? -- 5.4.2 Case Study on Data Augmentation -- 5.5 Data Deployment -- 5.5.1 Case Study on Data Deployment -- 5.6 Data-Centric AI Tools -- 5.6.1 Case Study: Predicting Customer Churn for a Telecommunications Company -- 5.7 Summary -- References -- 6 Data-Centric AI in Healthcare -- 6.1 Overview -- 6.2 Need and Challenges of Data-Centric Approach -- 6.3 Application Implementation in Data-Centric Approach -- 6.4 Application Implementation in Model-Centric Approach
001482407 5058_ $$a6.5 Comparison of Model-Centric AI and Data-Centric AI -- 6.6 Summary -- References -- 7 Data-Centric AI in Mechanical Engineering -- 7.1 Overview -- 7.2 Need and Challenges of Data-Centric Approach -- 7.3 Application Implementation in Data-Centric Approach -- 7.4 Application Implementation in Model-Centric Approach -- 7.5 Comparison of Model-Centric AI and Data-Centric AI -- 7.6 Case Study: Mechanical Tools Classification -- 7.7 Summary -- References -- 8 Data-Centric AI in Information, Communication and Technology -- 8.1 Overview -- 8.2 Need and Challenges of Data-Centric Approach
001482407 506__ $$aAccess limited to authorized users.
001482407 520__ $$aThis book discusses the best research roadmaps, strategies, and challenges in data-centric approach of artificial intelligence (AI) in various domains. It presents comparative studies of model-centric and data-centric AI. It also highlights different phases in data-centric approach and data-centric principles. The book presents prominent use cases of data-centric AI. It serves as a reference guide for researchers and practitioners in academia and industry.
001482407 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 23, 2023).
001482407 650_0 $$aArtificial intelligence.$$xMedical applications$$0(DLC)sh 88003000
001482407 650_0 $$aBig data.$$0(DLC)sh2012003227
001482407 655_0 $$aElectronic books.
001482407 7001_ $$aShinde, Gitanjali Rahul,$$d1983-
001482407 7001_ $$aIngle, Yashwant S.
001482407 7001_ $$aWasatkar, Namrata N.
001482407 77608 $$iPrint version:$$aMahalle, Parikshit N.$$tData Centric Artificial Intelligence: a Beginner's Guide$$dSingapore : Springer,c2023$$z9789819963522
001482407 830_0 $$aData-intensive research.
001482407 852__ $$bebk
001482407 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-6353-9$$zOnline Access$$91397441.1
001482407 909CO $$ooai:library.usi.edu:1482407$$pGLOBAL_SET
001482407 980__ $$aBIB
001482407 980__ $$aEBOOK
001482407 982__ $$aEbook
001482407 983__ $$aOnline
001482407 994__ $$a92$$bISE