Und dann auf "Zum Home-Bildschirm [+]".
Stöbern
genialokal zum Startbildschirm hinzufügen
Installieren Sie die genialokal App auf Ihrem Startbildschirm für einen schnellen Zugriff und eine komfortable Nutzung.
Installieren Sie die genialokal App auf Ihrem Startbildschirm für einen schnellen Zugriff und eine komfortable Nutzung.
Ihr gewünschter Artikel ist in 0 Buchhandlungen vorrätig - wählen Sie hier eine Buchhandlung in Ihrer Nähe aus:
5G technologies are enabling AI-based applications over the global network. This book provides a unified framework for the deep convergence of computing and communications, where the network and application/service can be jointly optimized as a single integrated system using AI. It presents a full treatment of 5G networked AI.
Radhika Ranjan Roy is an electronics engineer, US Army Research, Development, and Engineering Command (RDECOM), Communications-Electronics Research, Development, and Engineering Center (CERDEC), Space and Terrestrial Communications Directorate (S&TCD) Laboratories, Aberdeen Proving Ground (APG), Maryland, since 2009. Before joining to US Army Research, he worked in various capacities in CACI, SAIC, AT&T/Bell Laboratories, CSC, and PDB since his graduation. He earned his PhD in electrical engineering with major in computer communications from the City University of New York, New York, in 1984, and MS in electrical engineering from the Northeastern University, Boston, Massachusetts, in 1978. He received his BS in electrical engineering from the Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, in 1967. He has published more than 50 technical papers. He is holding and/or submitted over 30 patents. He authored a book titled Handbook of Mobile Ad Hoc Networks on Mobility Models in 2010.
1. Networked Artificial Intelligence 2. Artificial Intelligent Agent 3. Agent Function 4. Agent Modeling 5. Multi-Agent System 6. Protocol Layer Architecture 7. Artificial Intelligence Performance Analysis 8. Unsupervised Machine Learning 9. Supervised Machine Learning 10. Deep Learning 11. Overfitting and Underfitting 12. Hybrid Learning 13. Reinforcement Learning 14. Artificial Intelligence Application and Network Protocol Architecture Model 15. AI-Enabled Network 16. AI-Enabled End-to-End Network 17. AI-Enabled Peer-to-Peer Network 18. Artificial Intelligence-Enabled 5G Network
