• Title Learning Deep Architectures for AI
• Author(s) Yoshua Bengio
• Publisher: Now Publishers Inc (October 28, 2009)
• Hardcover/Paperback 144 pages
• eBook PDF (131 pages)
• Language: English
• ISBN-10: 1601982941
• ISBN-13: 978-1601982940
• Share This:  

Book Description

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.

• Yoshua Bengio is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA).

• Amazon

• Neural Networks and Deep Learning
• Machine Learning
• Data Science
• Artificial Intelligence
• Data Analysis and Data Mining

• Learning Deep Architectures for AI (Yoshua Bengio)
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF

• Understanding Deep Learning (Simon J.D. Prince)

  An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Only the most important ideas to provide a high density of critical information in an intuitive and digestible form.

• Manifold Learning: Model Reduction in Engineering

  The aim is to provide tools for a better understanding and implement reduced order models by using: physics-based models, synthetic data forecast by these models, experimental data and deep learning algorithms.

• The Principles of Deep Learning Theory: An Effective Approach

  This textbook establishes a theoretical framework for understanding deep learning models of practical relevance, provide clear and pedagogical explanations of how realistic deep neural networks actually work.

• The Little Book of Deep Learning (François Fleuret)

  This book is a short introduction to deep learning for readers with a STEM background, originally designed to be read on a phone screen, is limited to the background necessary to understand a few important models.

• The Shallow and the Deep: Introduction to Neural Networks

  This book is a collection of lecture notes that offers an accessible introduction to Neural Networks and machine learning in general. The focus lies on classical machine learning techniques, with a bias towards classification and regression.

• Gradient Expectations: Structure of Predictive Neural Networks

  An insightful investigation into the mechanisms underlying the predictive functions of neural networks - and their ability to chart a new path for AI. Delve into the known neural architecture of the mammalian brain to illuminate the structure of predictive networks.

• Mathematical Introduction to Deep Learning (Arnulf Jentzen, et al)

  This book aims to provide an introduction to the topic of deep learning algorithms, coverss essential components of deep learning algorithms in full mathematical detail including different Artificial Neural Network (ANN) architectures and algorithms.

• Deep Learning (Ian Goodfellow, et al)

  Written by three experts, this is the only comprehensive book on the subject. It offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.