Adaptive Computation and Machine Learning series: Knowledge Graphs
Fundamentals, Techniques, and Applications
Hodnotenie knihy
Parametre
- 568 stránok
- 20 hodin čítania
Viac o knihe
A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence.The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.
Nákup knihy
Adaptive Computation and Machine Learning series: Knowledge Graphs, Mayank Kejriwal, Pedro Szekely, Craig A. Knoblock
- Jazyk
- Rok vydania
- 2021
- Väzba
- (pevná)
Doručenie
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- Titul
- Adaptive Computation and Machine Learning series: Knowledge Graphs
- Podtitul
- Fundamentals, Techniques, and Applications
- Jazyk
- anglicky
- Vydavateľ
- MIT Press
- Rok vydania
- 2021
- Väzba
- pevná
- Počet strán
- 568
- ISBN10
- 0262045095
- ISBN13
- 9780262045094
- Série
- Kategórie
- Štítky
- Byznys, Prírodné vedy, Psychologická tematika, Filozofická tematika, Veda, Technológia, Vzdelávanie & školstvo, Škola, Spoločnosť, Vedenie, Komunikácia, Kultúra, Zamestnanie, Strojárstvo, Budúcnosť, Internet, Evolúcia, Umelá inteligencia, Neuroveda, Mozog, Stratégia, Vedomie, Inovácie, Databázy, Algoritmy, Kritické myslenie, Robotika, Strojové učenie, Hacking
- Hodnotenie
- 3,5 z 5
- Anotácia
- A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence.The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.