Online-Ressource
Verfasst von:	Buduma, Nithin [VerfasserIn]
	Buduma, Nikhil [VerfasserIn]
	Papa Joe [VerfasserIn]
Titel:	Fundamentals of deep learning
Titelzusatz:	designing next-generation machine intelligence algorithms
Mitwirkende:	Locascio, Nicholas [MitwirkendeR]
Verf.angabe:	Nithin Buduma, Nikhil Buduma, and Joe Papa ; with contributions by Nicholas Locascio
Ausgabe:	Second edition
Verlagsort:	Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
Verlag:	O'Reilly
Jahr:	2022
Umfang:	1 online resource (76 pages)
Fussnoten:	Online resource; Title from title page (viewed September 25, 2021)
Abstract:	We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception that has powered our push toward self-driving vehicles, the ability to defeat human experts at a variety of difficult games including Go and Starcraft, and even generate essays with shockingly coherent prose. But deciphering these breakthroughs often takes a Ph.D. education in machine learning and mathematics. This updated second edition describes the intuition behind these innovations without the jargon and complexity. By the end of this book, Python-proficient programmers, software engineering professionals, and computer science majors will be able to re-implement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best in the field. New chapters cover recent advancements in the fields of generative modeling and interpretability. Code examples throughout the book are updated to TensorFlow 2 and PyTorch 1.4.
ComputerInfo:	Mode of access: World Wide Web.
URL:	Aggregator: https://learning.oreilly.com/library/view/-/9781492082170/?ar
Schlagwörter:	(s)Deep learning
Datenträger:	Online-Ressource
Sprache:	eng
Sach-SW:	Electronic books ; local
K10plus-PPN:	1756029156
	Universitätsbibliothek
Lokale URL UB:	Zum Volltext
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Bibliothek/Idn:	UW / m3918252272
Lokale URL Inst.:	Zum Volltext