nakayama kiyoshi (59 risultati)

Hardcover. Condizione: As New. First Edition. Hardcover in dust jacket. Shows minor shelf wear, otherwise an unblemished copy!; 100% Satisfaction Guaranteed! Ships same or next business day!

Hardcover. Condizione: Very Good. Condizione sovraccoperta: Very Good. 1st Edition. The book is signed by editor Kiyoshis Nakayama on the ffe. The book is bound in light blue cloth with title on the spine in gilt. Top corner os the front cover is starting to roll. Sprinkling of foxing on the top edges and one sppot on the fore-edges. There is foxing on the top margin of the full title page and on top margin of the facing blank glossy page. Otherwise, Pages are clean, smooth and unmarked. The dj is in very good condition with foxing along the oper edges of the panels. It is unclipped with no original price indicated.

Magazine. pp67-126, 5.5x8.5 inches, essays, critiques, reviews, news in brief, ads, awards, very good literary journs, a booklet in stapled yellow wraps. Essays from many Japanese professors as well as professors from Indiana and the UK.

Hardcover. Condizione: Almost Like New. First Printing. Book is a clean tight unmarked copy, except for friend's message on front end paper.

Hardcover. Condizione: Very Good+. Condizione sovraccoperta: Very Good+. Written in English. Cloth covered boards. DJ with slight edgewear. Text clean and unmarked. Binding sound. No previous owner names, stickers or stamps. ; 8.66 X 6.22 X 0.79 inches; 230 pages.

Condizione: New.

Paperback or Softback. Condizione: New. Federated Learning with Python: Design and implement a federated learning system and develop applications using existing frameworks. Book.

Hardcover. Condizione: Very Good. Condizione sovraccoperta: Good. Osaka Kyoiku Tosho, Co., 1992. Hardback, d/j, 8vo, xvii,230pp. Inscribed and signed by editor (Kiyoshi Nakayama). A good copy. 427111622X/0.5jp . (Please note that our condition gradings are stricter than those of Abebooks and many other sellers. There may therefore be a discrepancy between this description and its listed condition grading).

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New. Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next levelKey FeaturesDesign distributed systems that can be applied to real-world federated learning applications at scaleDiscover multiple aggregation schemes applicable to various ML settings and applicationsDevelop a federated learning system that can be tested in distributed machine learning settingsBook DescriptionFederated learning (FL) is a paradigm-shifting technology in AI that enables and accelerates machine learning (ML), allowing you to work on private data. It has become a must-have solution for most enterprise industries, making it a critical part of your learning journey. This book helps you get to grips with the building blocks of FL and how the systems work and interact with each other using solid coding examples.FL is more than just aggregating collected ML models and bringing them back to the distributed agents. This book teaches you about all the essential basics of FL and shows you how to design distributed systems and learning mechanisms carefully so as to synchronize the dispersed learning processes and synthesize the locally trained ML models in a consistent manner. This way, you'll be able to create a sustainable and resilient FL system that can constantly function in real-world operations. This book goes further than simply outlining FL's conceptual framework or theory, as is the case with the majority of research-related literature.By the end of this book, you'll have an in-depth understanding of the FL system design and implementation basics and be able to create an FL system and applications that can be deployed to various local and cloud environments.What you will learnDiscover the challenges related to centralized big data ML that we currently face along with their solutionsUnderstand the theoretical and conceptual basics of FLAcquire design and architecting skills to build an FL systemExplore the actual implementation of FL servers and clientsFind out how to integrate FL into your own ML applicationUnderstand various aggregation mechanisms for diverse ML scenariosDiscover popular use cases and future trends in FLWho this book is forThis book is for machine learning engineers, data scientists, and artificial intelligence (AI) enthusiasts who want to learn about creating machine learning applications empowered by federated learning. You'll need basic knowledge of Python programming and machine learning concepts to get started with this book.

Paperback. Condizione: New. Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next levelKey FeaturesDesign distributed systems that can be applied to real-world federated learning applications at scaleDiscover multiple aggregation schemes applicable to various ML settings and applicationsDevelop a federated learning system that can be tested in distributed machine learning settingsBook DescriptionFederated learning (FL) is a paradigm-shifting technology in AI that enables and accelerates machine learning (ML), allowing you to work on private data. It has become a must-have solution for most enterprise industries, making it a critical part of your learning journey. This book helps you get to grips with the building blocks of FL and how the systems work and interact with each other using solid coding examples.FL is more than just aggregating collected ML models and bringing them back to the distributed agents. This book teaches you about all the essential basics of FL and shows you how to design distributed systems and learning mechanisms carefully so as to synchronize the dispersed learning processes and synthesize the locally trained ML models in a consistent manner. This way, you'll be able to create a sustainable and resilient FL system that can constantly function in real-world operations. This book goes further than simply outlining FL's conceptual framework or theory, as is the case with the majority of research-related literature.By the end of this book, you'll have an in-depth understanding of the FL system design and implementation basics and be able to create an FL system and applications that can be deployed to various local and cloud environments.What you will learnDiscover the challenges related to centralized big data ML that we currently face along with their solutionsUnderstand the theoretical and conceptual basics of FLAcquire design and architecting skills to build an FL systemExplore the actual implementation of FL servers and clientsFind out how to integrate FL into your own ML applicationUnderstand various aggregation mechanisms for diverse ML scenariosDiscover popular use cases and future trends in FLWho this book is forThis book is for machine learning engineers, data scientists, and artificial intelligence (AI) enthusiasts who want to learn about creating machine learning applications empowered by federated learning. You'll need basic knowledge of Python programming and machine learning concepts to get started with this book.

Condizione: New. pp. 326.

Hardcover. Condizione: Near Fine. Condizione sovraccoperta: Fine. Signed by all three editors!! Tips of corners show very faint wear. book and dust jacket are looking essentially as new. Jacket now protected in an archival cover.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Cloth. Condizione: Fine. Condizione sovraccoperta: Fine. First Thus. (iv) (2) 157 (1) pp. Illustrated from photographs of the 1937 George S. Kaufman Broadway Production. Illustrated dust jacket. Preface and Play in English. Extensive Notes in Japanese. Cloth with Japanese spine titling. 5 1/4" x 7 1/2". From the Preface: "This edition of Of Mice and men: A Play in Three Acts, aims at revitalizing interest in thje Nobel Laureate novelist John Steinbeck (1902-1968), as a playwright. tje new edition with notes and comments in Japanese is the first play version puvlished in Japan since the Nan'un-do's novel edition in 1954, one of the best selling Steinbeck books here." A Fine copy in a Fine dust jacket, now in a clear mylar protective sleeve.

Tankobon Hardcover. Condizione: Brand New. Japanese language. 10.20x7.28x0.39 inches. In Stock.

Hardcover. Condizione: near fine. 664 pages: illustrations; 25 cm; bibliographical references (pages 617-649) and index. . "Translation and study of the Season-granting system"--Page 5. Two sheets of "Emendations" laid in. Inscribed lengthily and signed by the author. / Sources and Studies in the History of Mathematics and Physical Sciences. [Series]. Contents: Introduction -- Astronomical reform and occupation politics -- Orientation -- The project: origins and process -- The astronomers -- The observatory and its instruments -- The records -- Evaluation of the Season-granting system -- Canon of the Season-granting system -- Appendix A: The instruments of Kuo Shou-ching -- Appendix B: The account of conduct of Kuo Shou-ching -- Appendix C: Technical terms. "China's most sophisticated system of computational astronomy was created for a Mongol emperor who could neither read nor write Chinese, to celebrate victory over China after forty years of devastating war. This book explains how and why, and reconstructs the working group, the observatory and the science that made it possible."?Publisher. ??"Nathan Sivin, born in 1931, is an American historian of Chinese science, medicine, and technology. He is known for his work in the field of Chinese history, particularly in the history of Chinese science and medicine. Sivin studied at the Massachusetts Institute of Technology (MIT) and the University of California, Berkeley, before becoming a professor at the University of Pennsylvania. He has written extensively on the history of Chinese science and its relationship with Chinese culture and society. One of his most well-known works is "Science in Ancient China: Researches and Reflections," in which he explores the development of scientific thought and practice in China, from antiquity to the modern era. He has also written about the history of Chinese medicine, including the practice of acupuncture and the development of medical theory. Sivin's work is characterized by a deep understanding of both Chinese and Western scientific traditions, and he has been instrumental in promoting a more nuanced understanding of the history of science that goes beyond a Eurocentric perspective." ?Bing ? Astronomie chinoise -- Histoire. Astronomy, Chinese. Identifier: Geschichte; 1280 Class Descriptors: LC: QB17; Dewey: 520.951 Other Titles: ????? Chinese astronomical reform of 1280; Shou shih li cong kao; Season-granting system. Responsibility: Nathan Sivin; with the research collaboration of the late Kiyosi Yabuuti ??? and Shigeru Nakayama ??? Nathan Sivin; with the research collaboration of the late Kiyosi Yabuuti, Yabuuchi Kiyosi, and Shigeru Nakayama, Nakayama Shigeru. Upper board corners gently bumped; spine lightly sunned; else tight clean and very goo(++) to near fine (-) thus.

Paperback. Condizione: New. Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next levelKey FeaturesDesign distributed systems that can be applied to real-world federated learning applications at scaleDiscover multiple aggregation schemes applicable to various ML settings and applicationsDevelop a federated learning system that can be tested in distributed machine learning settingsBook DescriptionFederated learning (FL) is a paradigm-shifting technology in AI that enables and accelerates machine learning (ML), allowing you to work on private data. It has become a must-have solution for most enterprise industries, making it a critical part of your learning journey. This book helps you get to grips with the building blocks of FL and how the systems work and interact with each other using solid coding examples.FL is more than just aggregating collected ML models and bringing them back to the distributed agents. This book teaches you about all the essential basics of FL and shows you how to design distributed systems and learning mechanisms carefully so as to synchronize the dispersed learning processes and synthesize the locally trained ML models in a consistent manner. This way, you'll be able to create a sustainable and resilient FL system that can constantly function in real-world operations. This book goes further than simply outlining FL's conceptual framework or theory, as is the case with the majority of research-related literature.By the end of this book, you'll have an in-depth understanding of the FL system design and implementation basics and be able to create an FL system and applications that can be deployed to various local and cloud environments.What you will learnDiscover the challenges related to centralized big data ML that we currently face along with their solutionsUnderstand the theoretical and conceptual basics of FLAcquire design and architecting skills to build an FL systemExplore the actual implementation of FL servers and clientsFind out how to integrate FL into your own ML applicationUnderstand various aggregation mechanisms for diverse ML scenariosDiscover popular use cases and future trends in FLWho this book is forThis book is for machine learning engineers, data scientists, and artificial intelligence (AI) enthusiasts who want to learn about creating machine learning applications empowered by federated learning. You'll need basic knowledge of Python programming and machine learning concepts to get started with this book.

Paperback. Condizione: New. Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next levelKey FeaturesDesign distributed systems that can be applied to real-world federated learning applications at scaleDiscover multiple aggregation schemes applicable to various ML settings and applicationsDevelop a federated learning system that can be tested in distributed machine learning settingsBook DescriptionFederated learning (FL) is a paradigm-shifting technology in AI that enables and accelerates machine learning (ML), allowing you to work on private data. It has become a must-have solution for most enterprise industries, making it a critical part of your learning journey. This book helps you get to grips with the building blocks of FL and how the systems work and interact with each other using solid coding examples.FL is more than just aggregating collected ML models and bringing them back to the distributed agents. This book teaches you about all the essential basics of FL and shows you how to design distributed systems and learning mechanisms carefully so as to synchronize the dispersed learning processes and synthesize the locally trained ML models in a consistent manner. This way, you'll be able to create a sustainable and resilient FL system that can constantly function in real-world operations. This book goes further than simply outlining FL's conceptual framework or theory, as is the case with the majority of research-related literature.By the end of this book, you'll have an in-depth understanding of the FL system design and implementation basics and be able to create an FL system and applications that can be deployed to various local and cloud environments.What you will learnDiscover the challenges related to centralized big data ML that we currently face along with their solutionsUnderstand the theoretical and conceptual basics of FLAcquire design and architecting skills to build an FL systemExplore the actual implementation of FL servers and clientsFind out how to integrate FL into your own ML applicationUnderstand various aggregation mechanisms for diverse ML scenariosDiscover popular use cases and future trends in FLWho this book is forThis book is for machine learning engineers, data scientists, and artificial intelligence (AI) enthusiasts who want to learn about creating machine learning applications empowered by federated learning. You'll need basic knowledge of Python programming and machine learning concepts to get started with this book.

Condizione: Fine. KOS01200914.

Condizione: Fine. Number of books: 1 book.

Condizione: Fine. Number of pages: 159p Size: A5 Number of books: 1 book.

Condizione: Fine. Number of pages: 159p Size: A5 Number of books: 1 book.

Condizione: Fine. Number of pages: 159p Size: A5 Number of books: 1 book.

Condizione: Fine. Number of books: 1.

Condizione: Fine. The book is in fine condition.

Condizione: Fine. Number of pages: 159p Size: A5 Number of books: 1 book.

Condizione: Fine. Number of books: 1 book.

Condizione: Fine. The book is in fine condition.