Learning Deep Architectures for AI is popular PDF and ePub book, written by Yoshua Bengio in 2009, it is a fantastic choice for those who relish reading online the Computational learning theory genre. Let's immerse ourselves in this engaging Computational learning theory book by exploring the summary and details provided below. Remember, Learning Deep Architectures for AI can be Read Online from any device for your convenience.
Learning Deep Architectures for AI Book PDF 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 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.
Detail Book of Learning Deep Architectures for AI PDF
- Author : Yoshua Bengio
- Release : 29 September 2024
- Publisher : Now Publishers Inc
- ISBN : 9781601982940
- Genre : Computational learning theory
- Total Page : 145 pages
- Language : English
- PDF File Size : 8,7 Mb
If you're still pondering over how to secure a PDF or EPUB version of the book Learning Deep Architectures for AI by Yoshua Bengio, don't worry! All you have to do is click the 'Get Book' buttons below to kick off your Download or Read Online journey. Just a friendly reminder: we don't upload or host the files ourselves.