| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910696557003321 |
|
|
Titolo |
Traffic safety [[electronic resource] ] : grants generally address key safety issues, despite state eligibility and management difficulties : report to the Committee on Transportation and Infrastructure, House of Representatives |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
[Washington, D.C.] : , : U.S. Govt. Accountability Office, , [2008] |
|
|
|
|
|
|
|
Descrizione fisica |
|
iii, 55 pages : digital, PDF file |
|
|
|
|
|
|
Soggetti |
|
Traffic safety - United States - States - Finance |
Traffic fatalities - United States - States - Prevention |
Grants-in-aid - United States |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Title from title screen (viewed on Apr. 17, 2008). |
"March 2008." |
Paper version available from: U.S. Govt. Accountability Office, 441 G St., NW, Rm. LM, Washington, D.C. 20548. |
"GAO-08-398." |
|
|
|
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references. |
|
|
|
|
|
|
|
|
|
|
|
|
|
2. |
Record Nr. |
UNINA9910743351903321 |
|
|
Titolo |
Distributed Machine Learning and Gradient Optimization / / by Jiawei Jiang, Bin Cui, Ce Zhang |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 |
|
|
|
|
|
|
|
ISBN |
|
981-16-3419-X |
981-16-3420-3 |
|
|
|
|
|
|
|
|
Edizione |
[1st ed. 2022.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (179 pages) |
|
|
|
|
|
|
Collana |
|
Big Data Management, , 2522-0187 |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Machine learning |
Data mining |
Database management |
Machine Learning |
Data Mining and Knowledge Discovery |
Database Management |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references. |
|
|
|
|
|
|
Nota di contenuto |
|
1 Introduction -- 2 Basics of Distributed Machine Learning -- 3 Distributed Gradient Optimization Algorithms -- 4 Distributed Machine Learning Systems -- 5 Conclusion. . |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of |
|
|
|
|
|
|
|
|
|
|
distributed machine learning. It will appeal toa broad audience in the field of machine learning, artificial intelligence, big data and database management. |
|
|
|
|
|
| |