Artificial Intelligence and Machine Learning for EDGE Computing

Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints INDICE: Part 1: AI and Machine Learning 1. Artificial Intelligence 2. Machine Learning 3. Regression Analysis 4. Bayesian Statistics 5. Learning Theory 6. Supervised Learning 7. Unsupervised Learning 8. Reinforcement Learning 9. Instance Based Learning and Feature Engineering Part 2: Data Science and Predictive Analysis 10. Introduction to Data Science and Analysis 11. Linear Algebra, Statistics, Probability, Hypothesis and Inference, Gradient Descent 12. Predictive Analysis Part 3: Edge Computing 13. Distributed Computing - Cloud to fog to Edge 14. Edge Computing 15. Integrating AI with Edge Computing 16. Machine learning integration with Edge Computing 17. Applying AI/Ml at the edge

• ISBN: 978-0-12-824054-0
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 484
• Fecha Publicación: 29/04/2022
• Nº Volúmenes: 1
• Idioma: Inglés