| Autore |
Mahalle Parikshit N
|
| Edizione | [1st ed.] |
| Pubbl/distr/stampa |
Singapore : , : Springer, , 2023
|
| Descrizione fisica |
1 online resource (137 pages)
|
| Altri autori (Persone) |
ShindeGitanjali R
IngleYashwant S
WasatkarNamrata N
|
| Collana |
Data-Intensive Research Series
|
| ISBN |
981-9963-53-2
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Intro -- 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 -- 2.6 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 -- 4.3.2 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 -- 5.3.2 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 -- 6.5 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 -- 8.3 Application Implementation in Data-Centric Approach -- 8.4 Application Implementation in Model-Centric Approach -- 8.5 Comparison of Model-Centric AI and Data-Centric AI -- 8.6 Summary -- References -- 9 Conclusion -- 9.1 Summary -- 9.2 Research Areas -- References.
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| Record Nr. | UNINA-9910751383203321 |
Info
Cognitive Computing for Machine Thinking
