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Deep Learning
  • Language: en
  • Pages: 800

Deep Learning

  • Type: Book
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  • Published: 2016-11-18
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  • Publisher: MIT Press

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

Learning Deep Architectures for AI
  • Language: en
  • Pages: 131

Learning Deep Architectures for AI

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.

Neural Networks for Speech and Sequence Recognition
  • Language: en
  • Pages: 167

Neural Networks for Speech and Sequence Recognition

Sequence recognition is a crucial element in many applications in the fields of speech analysis, control, and modeling. This book applies the techniques of neural networks and hidden Markov models to the problems of sequence recognition, and as such will prove valuable to researchers and graduate students alike.

Yoshua Bengio
  • Language: en
  • Pages: 110

Yoshua Bengio

  • Type: Book
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  • Published: 2017-10-26
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  • Publisher: Unknown

Biography of Yoshua Bengio, currently Full professor at Universite de Montreal, previously Post-doc at AT&T Labs, Inc. and Post-doc at AT&T Labs, Inc.

Architects of Intelligence
  • Language: en
  • Pages: 554

Architects of Intelligence

Book Description How will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances? Architects of Intelligence contains a series of in-depth, one-to-one interviews where New York Times bestselling author, Martin Ford, uncovers the truth behind these questions from some of the brightest minds in the Artificial Intelligence community. Martin has wide-ranging conversations with twenty-three of the world's foremost researchers and entrepreneurs working in AI and robotics: Demis Hassabis (DeepMind), Ray Kur...

Artificial Neural Networks and Their Applications
  • Language: en
  • Pages: 167
Large-scale Kernel Machines
  • Language: en
  • Pages: 396

Large-scale Kernel Machines

  • Type: Book
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  • Published: 2007
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  • Publisher: MIT Press

Topics covered include fast implementations of known algorithms, approximations that are amenable to theoretical guarantees, and algorithms that perform well in practice but are difficult to analyze theoretically.

Artificial Neural Networks and Their Application to Sequence Recognition
  • Language: en
  • Pages: 352

Artificial Neural Networks and Their Application to Sequence Recognition

  • Type: Book
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  • Published: 1991
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  • Publisher: Unknown

"This thesis studies the introduction of a priori structure into the design of learning systems based on artificial neural networks applied to sequence recognition, in particular to phoneme recognition in continuous speech. Because we are interested in sequence analysis, algorithms for training recurrent networks are studied and an original algorithm for constrained recurrent networks is proposed and test results are reported. We also discuss the integration of connectionist models with other analysis tools that have been shown to be useful for sequences, such as dynamic programming and hidden Markov models. We introduce an original algorithm to perform global optimization of a neural network/hidden Markov model hybrid, and show how to perform such a global optimization on all the parameters of the system. Finally, we consider some alternatives to sigmoid networks: Radial Basis Functions, and a method for searching for better learning rules using a priori knowledge and optimization algorithms." --

Artificial Neural Networks and Their Applications
  • Language: en
  • Pages: 167
Optimization for Machine Learning
  • Language: en
  • Pages: 494

Optimization for Machine Learning

  • Type: Book
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  • Published: 2012
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  • Publisher: MIT Press

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessi...