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Englisch
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Beschreibung
Written by an expert who has spent 40 years treating couples who had fertility challenges, this book provides a clear explanation of what a couple need to conceive, and how to deal with problems which may be encountered. A recommended read for those wanting to conceive, their friends and families, and anyone interested in reproduction.
Written by an expert who has spent 40 years treating couples who had fertility challenges, this book provides a clear explanation of what a couple need to conceive, and how to deal with problems which may be encountered. A recommended read for those wanting to conceive, their friends and families, and anyone interested in reproduction.
Über den Autor
Weizmann Institute of Science, Israel
Inhaltsverzeichnis
Preface; 1. Machine learning and communications: an introduction Deniz Gündüz, Yonina Eldar, Andrea Goldsmith and H. Vincent Poor; Part I. Machine Learning for Wireless Networks: 2. Deep neural networks for joint source-channel coding David Burth Kurka, Milind Rao, Nariman Farsad, Deniz Gündüz and Andrea Goldsmith; 3. Neural network coding Litian Liu, Amit Solomon, Salman Salamatian, Derya Malak and Muriel Medard; 4. Channel coding via machine learning Hyeji Kim; 5. Channel estimation, feedback and signal detection Hengtao He, Hao Ye, Shi Jin and Geoffrey Y. Li; 6. Model-based machine learning for communications Nir Shlezinger, Nariman Farsad, Yonina Eldar and Andrea Goldsmith; 7. Constrained unsupervised learning for wireless network optimization Hoon Lee, Sang Hyun Lee and Tony Q. S. Quek; 8. Radio resource allocation in smart radio environments Alessio Zappone and Mérouane Debbah; 9. Reinforcement learning for physical layer communications Philippe Mary, Christophe Moy and Visa Koivunen; 10. Data-driven wireless networks: scalability and uncertainty Feng Yin, Yue Xu and Shuguang Cui; 11. Capacity estimation using machine learning Ziv Aharoni, Dor Zur, Ziv Goldfeld and Haim Permuter; Part II. Wireless Networks for Machine Learning: 12. Collaborative learning on wireless networks: an introductory overview Mehmet Emre Ozfatura, Deniz Gündüz and H. Vincent Poor; 13. Optimized federated learning in wireless networks with constrained resources Shiqiang Wang, Tiffany Tuor and Kin K. Leung; 14. Quantized federated learning Nir Shlezinger, Mingzhe Chen, Yonina Eldar, H. Vincent Poor and Shuguang Cui; 15. Over-the-air computation for distributed learning over wireless networks Mohammad Mohammadi Amiri and Deniz Gündüz; 16. Federated knowledge distillation Hyowoon Seo, Seungeun Oh, Jihong Park, Seong-Lyun Kim and Mehdi Bennis; 17. Differentially private wireless federated learning Dongzhu Liu, Amir Sonee, Stefano Rini and Osvaldo Simeone; 18. Timely wireless edge inference Sheng Zhou, Wenqi Shi, Xiufeng Huang and Zhisheng Niu.
Details
Erscheinungsjahr: | 2022 |
---|---|
Fachbereich: | Nachrichtentechnik |
Genre: | Technik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Buch |
Seiten: | 554 |
Inhalt: | Gebunden |
ISBN-13: | 9781108832984 |
ISBN-10: | 1108832989 |
Sprache: | Englisch |
Einband: | Gebunden |
Autor: |
Eldar, Yonina C.
Goldsmith, Andrea Gündüz, Deniz Poor, H. Vincent |
Redaktion: |
Eldar, Yonina C
Goldsmith, Andrea Gündüz, Deniz Poor, H Vincent |
Hersteller: | Cambridge University Press |
Maße: | 247 x 172 x 30 mm |
Von/Mit: | Yonina C Eldar (u. a.) |
Erscheinungsdatum: | 03.11.2022 |
Gewicht: | 1,194 kg |
Über den Autor
Weizmann Institute of Science, Israel
Inhaltsverzeichnis
Preface; 1. Machine learning and communications: an introduction Deniz Gündüz, Yonina Eldar, Andrea Goldsmith and H. Vincent Poor; Part I. Machine Learning for Wireless Networks: 2. Deep neural networks for joint source-channel coding David Burth Kurka, Milind Rao, Nariman Farsad, Deniz Gündüz and Andrea Goldsmith; 3. Neural network coding Litian Liu, Amit Solomon, Salman Salamatian, Derya Malak and Muriel Medard; 4. Channel coding via machine learning Hyeji Kim; 5. Channel estimation, feedback and signal detection Hengtao He, Hao Ye, Shi Jin and Geoffrey Y. Li; 6. Model-based machine learning for communications Nir Shlezinger, Nariman Farsad, Yonina Eldar and Andrea Goldsmith; 7. Constrained unsupervised learning for wireless network optimization Hoon Lee, Sang Hyun Lee and Tony Q. S. Quek; 8. Radio resource allocation in smart radio environments Alessio Zappone and Mérouane Debbah; 9. Reinforcement learning for physical layer communications Philippe Mary, Christophe Moy and Visa Koivunen; 10. Data-driven wireless networks: scalability and uncertainty Feng Yin, Yue Xu and Shuguang Cui; 11. Capacity estimation using machine learning Ziv Aharoni, Dor Zur, Ziv Goldfeld and Haim Permuter; Part II. Wireless Networks for Machine Learning: 12. Collaborative learning on wireless networks: an introductory overview Mehmet Emre Ozfatura, Deniz Gündüz and H. Vincent Poor; 13. Optimized federated learning in wireless networks with constrained resources Shiqiang Wang, Tiffany Tuor and Kin K. Leung; 14. Quantized federated learning Nir Shlezinger, Mingzhe Chen, Yonina Eldar, H. Vincent Poor and Shuguang Cui; 15. Over-the-air computation for distributed learning over wireless networks Mohammad Mohammadi Amiri and Deniz Gündüz; 16. Federated knowledge distillation Hyowoon Seo, Seungeun Oh, Jihong Park, Seong-Lyun Kim and Mehdi Bennis; 17. Differentially private wireless federated learning Dongzhu Liu, Amir Sonee, Stefano Rini and Osvaldo Simeone; 18. Timely wireless edge inference Sheng Zhou, Wenqi Shi, Xiufeng Huang and Zhisheng Niu.
Details
Erscheinungsjahr: | 2022 |
---|---|
Fachbereich: | Nachrichtentechnik |
Genre: | Technik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Buch |
Seiten: | 554 |
Inhalt: | Gebunden |
ISBN-13: | 9781108832984 |
ISBN-10: | 1108832989 |
Sprache: | Englisch |
Einband: | Gebunden |
Autor: |
Eldar, Yonina C.
Goldsmith, Andrea Gündüz, Deniz Poor, H. Vincent |
Redaktion: |
Eldar, Yonina C
Goldsmith, Andrea Gündüz, Deniz Poor, H Vincent |
Hersteller: | Cambridge University Press |
Maße: | 247 x 172 x 30 mm |
Von/Mit: | Yonina C Eldar (u. a.) |
Erscheinungsdatum: | 03.11.2022 |
Gewicht: | 1,194 kg |
Warnhinweis