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Cognitive Engineering for Next Generation Computing
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Страница 1
Table of Contents
List of Illustrations
List of Table
Guide
Pages
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1
Introduction to Cognitive Computing
1.1 Introduction: Definition of Cognition, Cognitive Computing
1.2 Defining and Understanding Cognitive Computing
1.3 Cognitive Computing Evolution and Importance
1.4 Difference Between Cognitive Computing and Artificial Intelligence
1.5 The Elements of a Cognitive System
1.5.1 Infrastructure and Deployment Modalities
1.5.2 Data Access, Metadata, and Management Services
1.5.3 The Corpus, Taxonomies, and Data Catalogs
1.5.4 Data Analytics Services
1.5.5 Constant Machine Learning
1.5.6 Components of a Cognitive System
1.5.7 Building the Corpus
1.5.8 Corpus Administration Governing and Protection Factors
1.6 Ingesting Data Into Cognitive System
1.6.1 Leveraging Interior and Exterior Data Sources
1.6.2 Data Access and Feature Extraction
1.7 Analytics Services
1.8 Machine Learning
1.9 Machine Learning Process 1.9.1 Data Collection
1.9.2 Data Preparation
1.9.3 Choosing a Model
1.9.4 Training the Model
1.9.5 Evaluate the Model
1.9.6 Parameter Tuning
1.9.7 Make Predictions
1.10 Machine Learning Techniques
1.10.1 Supervised Learning
1.10.2 Unsupervised Learning
1.10.3 Reinforcement Learning
1.10.4 The Significant Challenges in Machine Learning
1.11 Hypothesis Space
1.11.1 Hypothesis Generation
1.11.2 Hypotheses Score
1.12 Developing a Cognitive Computing Application
1.13 Building a Health Care Application
1.13.1 Healthcare Ecosystem Constituents
1.13.2 Beginning With a Cognitive Healthcare Application
1.13.3 Characterize the Questions Asked by the Clients
1.13.4 Creating a Corpus and Ingesting the Content
1.13.5 Training the System
1.13.6 Applying Cognition to Develop Health and Wellness
1.13.7 Welltok
1.13.8 CaféWell Concierge in Action
1.14 Advantages of Cognitive Computing
1.15 Features of Cognitive Computing
1.16 Limitations of Cognitive Computing
1.17 Conclusion
References
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