The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks - Rilegato

Roberts, Daniel A.; Yaida, Sho

Rilegato

ISBN 10: 1316519333 ISBN 13: 9781316519332

Casa editrice: Cambridge University Press, 2022

Riassunto

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

Su questo libro

Informazioni sugli autori

Daniel A. Roberts was cofounder and CTO of Diffeo, an AI company acquired by Salesforce; a research scientist at Facebook AI Research; and a member of the School of Natural Sciences at the Institute for Advanced Study in Princeton, NJ. He was a Hertz Fellow, earning a PhD from MIT in theoretical physics, and was also a Marshall Scholar at Cambridge and Oxford Universities.

Sho Yaida is a research scientist at Meta AI. Prior to joining Meta AI, he obtained his PhD in physics at Stanford University and held postdoctoral positions at MIT and at Duke University. At Meta AI, he uses tools from theoretical physics to understand neural networks, the topic of this book.

Boris Hanin is an Assistant Professor at Princeton University in the Operations Research and Financial Engineering Department. Prior to joining Princeton in 2020, Boris was an Assistant Professor at Texas A&M in the Math Department and an NSF postdoc at MIT. He has taught graduate courses on the theory and practice of deep learning at both Texas A&M and Princeton.

Dettagli dell'edizione

• Editore: Cambridge University Press
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 1316519333
• ISBN 13: 9781316519332
• Rilegatura: Copertina rigida
• Numero di pagine: 472
• Contatto del produttore: non disponibile
• Persona responsabile: gpsr@libri.de; gpsr@libri.de; ; Friedensallee 273; Hamburg; 22763; Germania