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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.

Deep Learning
  • Language: en
  • Pages: 800

Deep Learning

  • Type: Book
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  • Published: 2016-11-10
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  • Publisher: MIT Press

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of...

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.

Artificial Neural Networks and Their Application to Sequence Recognition
  • Language: en
  • Pages: 256

Artificial Neural Networks and Their Application to Sequence Recognition

  • Type: Book
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  • Published: 1995-01-01
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  • Publisher: Unknown

None

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.

Learning from Partial Labels with Minimum Entropy
  • Language: en
  • Pages: 20
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

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 accessible to researchers in both fields.Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and ...

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

Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets.