The book describes development of software for Artificial Intelligence (AI) from the perspective of startups, industry and academia. It will empower the reader to create state of art AI applications that can be deployed for any computation platform (embedded or hand-held).
The book describes development of software for Artificial Intelligence (AI) from the perspective of startups, industry and academia. It will empower the reader to create state of art AI applications that can be deployed for any computation platform (embedded or hand-held).Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Chandrasekar Vuppalapati graduated from San Jose State University Masters Program, specializing Software Engineering, and completed his Master of Business Administration from Santa Clara University, Santa Clara, California, USA. He is a Software IT Executive and Entrepreneur with diverse experience in Software Technologies, Enterprise Software Architectures, Cloud Computing, Data Analytics, Internet of Things (IoT), and Software Product & Program Management. Chandra has held engineering architectures and product leadership roles at Microsoft, GE Healthcare, Cisco Systems, St. Jude Medical, and Lucent Technologies, a Bell Laboratories Company. He teaches Software Engineering, Large Scale Analytics, Data Science, Mobile Technologies, Cloud Technologies, and Web & Data Mining for Masters program in San Jose State University. Chandra has also held market research, strategy and technology architecture advisory roles in Cisco Systems, Lam Research and performed Principal Investigator role for Valley School of Nursing where he connected Nursing Educators & Students with Virtual Reality technologies. He has authored several international conference papers and published book on Building Enterprise IoT Applications. Chandra has served as Chair in numerous technology and advanced computing conferences such as: IEEE Oxford, UK, IEEE Big Data Services 2017, San Francisco USA, Future of Information and Communication Conference 2018, Singapore and Intelligent Human Systems Integration (IHSI) 2020, Modena, Italy.
Inhaltsangabe
SECTION I - INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND FRAMEWORKS Introduction What is AI? AI Epoch's: Waves of Compute AI Hype Cycle - Current and Emerging Technologies AI - End-To-End (E2E) Process - Turning Data into Actionable Insights Microsoft Azure - AI E2E Platform AI Development Operations (DevOps) Loop for Data Science AI -Performance and Computational Notations AI for Greater Good - Solving Humanity and Societal Challenges References Standard Processes and Frameworks Digital Transformation Digital Feedback Loop Insights Value Chain The CRISP-DM Process Building Blocks of AI - Major Components of AI AI Reference Architectures References SECTION II - DATA SOURCES AND ENGINEERING TOOLS Data - Call for Democratization Call for Action The Last Mile - Constrained Compute Devices AND "AI Chasm" References Machine Learning Frameworks and Device Engineering Machine Learning Device Deployments xRC Modeling: Model Accuracy-Connectivity-Hardware (MCH) Framework Circular Buffers AI Democratization - "Crossing the Chasm" References Device Software and Hardware Engineering Tools Software Engineering Tools Hardware and Engineering Tools Libraries References SECTION III - MODEL DEVELOPMENT AND DEPLOYMENT Supervised Models Decision Trees XGBoost Random Forrest Naïve Bayesian Linear Regression Kalman Filter References Unsupervised Models Hierarchical Clustering K-Means Clustering References SECTION IV - DEMOCRATIZATION AND FUTURE OF AI National Strategies National Technology Strategies for Serving People The United Nations AI Technology Strategy The role of the UN AI in the Hands of People References Future Democratization of Artificial Intelligence for the Future of Humanity Dedication Acknowledgement Preface Appendix Index
SECTION I - INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND FRAMEWORKS Introduction What is AI? AI Epoch's: Waves of Compute AI Hype Cycle - Current and Emerging Technologies AI - End-To-End (E2E) Process - Turning Data into Actionable Insights Microsoft Azure - AI E2E Platform AI Development Operations (DevOps) Loop for Data Science AI -Performance and Computational Notations AI for Greater Good - Solving Humanity and Societal Challenges References Standard Processes and Frameworks Digital Transformation Digital Feedback Loop Insights Value Chain The CRISP-DM Process Building Blocks of AI - Major Components of AI AI Reference Architectures References SECTION II - DATA SOURCES AND ENGINEERING TOOLS Data - Call for Democratization Call for Action The Last Mile - Constrained Compute Devices AND "AI Chasm" References Machine Learning Frameworks and Device Engineering Machine Learning Device Deployments xRC Modeling: Model Accuracy-Connectivity-Hardware (MCH) Framework Circular Buffers AI Democratization - "Crossing the Chasm" References Device Software and Hardware Engineering Tools Software Engineering Tools Hardware and Engineering Tools Libraries References SECTION III - MODEL DEVELOPMENT AND DEPLOYMENT Supervised Models Decision Trees XGBoost Random Forrest Naïve Bayesian Linear Regression Kalman Filter References Unsupervised Models Hierarchical Clustering K-Means Clustering References SECTION IV - DEMOCRATIZATION AND FUTURE OF AI National Strategies National Technology Strategies for Serving People The United Nations AI Technology Strategy The role of the UN AI in the Hands of People References Future Democratization of Artificial Intelligence for the Future of Humanity Dedication Acknowledgement Preface Appendix Index
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