skip to content
Learning deep architectures for AI Preview this item
ClosePreview this item
Checking...

Learning deep architectures for AI

Author: Yoshua Bengio
Publisher: Hanover, Mass. : Now Publishers, ©2009.
Series: Foundations and trends in machine learning (Online), v. 2, issue 1, p. 1-127.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
Theoretical results 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 may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching  Read more...
Rating:

(not yet rated) 0 with reviews - Be the first.

Subjects
More like this

 

Find a copy online

Links to this item

Find a copy in the library

&AllPage.SpinnerRetrieving; Finding libraries that hold this item...

Details

Genre/Form: Electronic books
Additional Physical Format: Print version:
Bengio, Yoshua.
Learning deep architectures for AI.
Hanover, Mass. : Now, ©2009, c2009
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Yoshua Bengio
ISBN: 9781601982957 160198295X 1601982941 9781601982940
OCLC Number: 678004184
Description: 1 online resource (127 pages : illustrations (some color)).
Contents: Abstract --
1. Introduction --
2. Theoretical advantages of deep architectures --
3. Local vs non-local generalization --
4. Neural networks for deep architectures --
5. Energy-based models and Boltzmann machines --
6. Greedy layer-wise training of deep architectures --
7. Variants of RBMs and auto-encoders --
8. Stochastic variational bounds for joint optimization of DBN layers --
9. Looking forward --
10. Conclusion --
Acknowledgments --
References.
Series Title: Foundations and trends in machine learning (Online), v. 2, issue 1, p. 1-127.
Responsibility: by Yoshua Bengio.

Abstract:

Learning Deep Architectures for AI discusses the motivations for and principles of learning algorithms for deep architectures.  Read more...

Reviews

User-contributed reviews
Retrieving GoodReads reviews...
Retrieving DOGObooks reviews...

Tags

Be the first.
Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Linked Data


Primary Entity

<http://www.worldcat.org/oclc/678004184> # Learning deep architectures for AI
    a schema:MediaObject, schema:CreativeWork, schema:Book ;
   library:oclcnum "678004184" ;
   library:placeOfPublication <http://experiment.worldcat.org/entity/work/data/693075539#Place/hanover_mass> ; # Hanover, Mass.
   library:placeOfPublication <http://id.loc.gov/vocabulary/countries/mau> ;
   schema:about <http://id.worldcat.org/fast/871997> ; # Computational learning theory
   schema:about <http://experiment.worldcat.org/entity/work/data/693075539#Topic/computers_enterprise_applications_business_intelligence_tools> ; # COMPUTERS--Enterprise Applications--Business Intelligence Tools
   schema:about <http://dewey.info/class/006.31/e22/> ;
   schema:about <http://experiment.worldcat.org/entity/work/data/693075539#Topic/computers_intelligence_ai_&_semantics> ; # COMPUTERS--Intelligence (AI) & Semantics
   schema:bookFormat schema:EBook ;
   schema:copyrightYear "2009" ;
   schema:creator <http://viaf.org/viaf/2365353> ; # Yoshua Bengio
   schema:datePublished "2009" ;
   schema:description "Abstract -- 1. Introduction -- 2. Theoretical advantages of deep architectures -- 3. Local vs non-local generalization -- 4. Neural networks for deep architectures -- 5. Energy-based models and Boltzmann machines -- 6. Greedy layer-wise training of deep architectures -- 7. Variants of RBMs and auto-encoders -- 8. Stochastic variational bounds for joint optimization of DBN layers -- 9. Looking forward -- 10. Conclusion -- Acknowledgments -- References."@en ;
   schema:description "Theoretical results 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 may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks."@en ;
   schema:exampleOfWork <http://worldcat.org/entity/work/id/693075539> ;
   schema:genre "Electronic books"@en ;
   schema:inLanguage "en" ;
   schema:isPartOf <http://worldcat.org/issn/1935-8245> ; # Foundations and trends in machine learning (Online) ;
   schema:isSimilarTo <http://worldcat.org/entity/work/data/693075539#CreativeWork/learning_deep_architectures_for_ai> ;
   schema:name "Learning deep architectures for AI"@en ;
   schema:productID "678004184" ;
   schema:publication <http://www.worldcat.org/title/-/oclc/678004184#PublicationEvent/hanover_mass_now_publishers_2009> ;
   schema:publisher <http://experiment.worldcat.org/entity/work/data/693075539#Agent/now_publishers> ; # Now Publishers
   schema:url <http://public.eblib.com/choice/publicfullrecord.aspx?p=3383670> ;
   schema:url <http://site.ebrary.com/id/10437482> ;
   schema:url <http://dx.doi.org/10.1561/2200000006> ;
   schema:url <http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=352823> ;
   schema:workExample <http://worldcat.org/isbn/9781601982957> ;
   schema:workExample <http://worldcat.org/isbn/9781601982940> ;
   schema:workExample <http://dx.doi.org/10.1561/2200000006> ;
   wdrs:describedby <http://www.worldcat.org/title/-/oclc/678004184> ;
    .


Related Entities

<http://experiment.worldcat.org/entity/work/data/693075539#Topic/computers_enterprise_applications_business_intelligence_tools> # COMPUTERS--Enterprise Applications--Business Intelligence Tools
    a schema:Intangible ;
   schema:name "COMPUTERS--Enterprise Applications--Business Intelligence Tools"@en ;
    .

<http://experiment.worldcat.org/entity/work/data/693075539#Topic/computers_intelligence_ai_&_semantics> # COMPUTERS--Intelligence (AI) & Semantics
    a schema:Intangible ;
   schema:name "COMPUTERS--Intelligence (AI) & Semantics"@en ;
    .

<http://id.worldcat.org/fast/871997> # Computational learning theory
    a schema:Intangible ;
   schema:name "Computational learning theory"@en ;
    .

<http://viaf.org/viaf/2365353> # Yoshua Bengio
    a schema:Person ;
   schema:familyName "Bengio" ;
   schema:givenName "Yoshua" ;
   schema:name "Yoshua Bengio" ;
    .

<http://worldcat.org/entity/work/data/693075539#CreativeWork/learning_deep_architectures_for_ai>
    a schema:CreativeWork ;
   rdfs:label "Learning deep architectures for AI." ;
   schema:description "Print version:" ;
   schema:isSimilarTo <http://www.worldcat.org/oclc/678004184> ; # Learning deep architectures for AI
    .

<http://worldcat.org/isbn/9781601982940>
    a schema:ProductModel ;
   schema:isbn "1601982941" ;
   schema:isbn "9781601982940" ;
    .

<http://worldcat.org/isbn/9781601982957>
    a schema:ProductModel ;
   schema:isbn "160198295X" ;
   schema:isbn "9781601982957" ;
    .

<http://worldcat.org/issn/1935-8245> # Foundations and trends in machine learning (Online) ;
    a bgn:PublicationSeries ;
   schema:hasPart <http://www.worldcat.org/oclc/678004184> ; # Learning deep architectures for AI
   schema:issn "1935-8245" ;
   schema:name "Foundations and trends in machine learning (Online) ;" ;
   schema:name "Foundations and trends in machine learning," ;
    .


Content-negotiable representations

Close Window

Please sign in to WorldCat 

Don't have an account? You can easily create a free account.