Learning Deep Architectures for AI
by Yoshua Bengio
Publisher: Now Publishers 2009
ISBN/ASIN: 1601982941
ISBN-13: 9781601982940
Number of pages: 130
Description:
This monograph 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.
Download or read it online for free here:
Download link
(1.1MB, PDF)
Similar books
Information Theory, Inference, and Learning Algorithmsby David J. C. MacKay - Cambridge University Press
A textbook on information theory, Bayesian inference and learning algorithms, useful for undergraduates and postgraduates students, and as a reference for researchers. Essential reading for students of electrical engineering and computer science.
(28100 views)
Bayesian Reasoning and Machine Learningby David Barber - Cambridge University Press
The book is designed for final-year undergraduate students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basics to advanced techniques within the framework of graphical models.
(21836 views)
The Elements of Statistical Learning: Data Mining, Inference, and Predictionby T. Hastie, R. Tibshirani, J. Friedman - Springer
This book brings together many of the important new ideas in learning, and explains them in a statistical framework. The authors emphasize the methods and their conceptual underpinnings rather than their theoretical properties.
(39436 views)
Machine Learning for Data Streamsby Albert Bifet, et al. - The MIT Press
This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA, allowing readers to try out the techniques after reading the explanations.
(6022 views)