Learning Deep Architectures for AI / Nejlevnější knihy
Learning Deep Architectures for AI

Kód: 04834775

Learning Deep Architectures for AI

Autor Yoshua Bengio

Can machine learning deliver AI? Theoretical results, inspiration from the brain and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that can represent high-leve ... celý popis

2483


Skladem u dodavatele
Odesíláme za 14-18 dnů
Přidat mezi přání

Mohlo by se vám také líbit

Darujte tuto knihu ještě dnes
  1. Objednejte knihu a zvolte Zaslat jako dárek.
  2. Obratem obdržíte darovací poukaz na knihu, který můžete ihned předat obdarovanému.
  3. Knihu zašleme na adresu obdarovaného, o nic se nestaráte.

Více informací

Více informací o knize Learning Deep Architectures for AI

Nákupem získáte 248 bodů

Anotace knihy

Can machine learning deliver AI? Theoretical results, inspiration from the brain and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one would need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers, graphical models with many levels of latent variables, or in complicated propositional formulae re-using many sub-formulae. Each level of the architecture represents features at a different level of abstraction, defined as a composition of lower-level features. Searching the parameter space of deep architectures is a difficult task, but new algorithms have been discovered and a new sub-area has emerged in the machine learning community since 2006, following these discoveries. Learning algorithms such as those for Deep Belief Networks and other related unsupervised learning algorithms have recently been proposed to train deep architectures, yielding exciting results and beating the state-of-the-art in certain areas. Learning Deep Architectures for AI discusses the motivations for and principles of learning algorithms for deep architectures. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed and discussed, highlighting challenges and suggesting avenues for future explorations in this area.

Parametry knihy

Zařazení knihy Knihy v angličtině Computing & information technology Computer science Mathematical theory of computation

2483

Oblíbené z jiného soudku



Osobní odběr Praha, Brno a 12903 dalších

Copyright ©2008-24 nejlevnejsi-knihy.cz Všechna práva vyhrazenaSoukromíCookies


Můj účet: Přihlásit se
Všechny knihy světa na jednom místě. Navíc za skvělé ceny.

Nákupní košík ( prázdný )

Vyzvednutí v Zásilkovně
zdarma nad 1 499 Kč.

Nacházíte se: