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Deep learning in neural networks: an overview.

Jürgen Schmidhuber1

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Neural Networks : the Official Journal of the International Neural Network Society
|December 3, 2014
PubMed
Summary
This summary is machine-generated.

This survey reviews deep artificial neural networks, distinguishing shallow and deep learners by credit assignment paths. It covers supervised, unsupervised, and reinforcement learning, plus evolutionary computation for pattern recognition and machine learning.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep artificial neural networks have achieved success in pattern recognition and machine learning.
  • Distinguishing between shallow and deep learners is crucial for understanding their capabilities.
  • Previous research, including work from the last millennium, forms the foundation for current deep learning models.

Purpose of the Study:

  • To provide a historical survey of deep artificial neural networks.
  • To differentiate between shallow and deep learning architectures based on credit assignment paths.
  • To review various deep learning paradigms and related computational methods.

Main Methods:

  • Historical review of relevant literature on artificial neural networks.
  • Analysis of credit assignment paths to distinguish learning models.
  • Summarization of deep supervised learning, unsupervised learning, and reinforcement learning techniques.

Main Results:

  • Deep learners are characterized by deeper credit assignment paths compared to shallow learners.
  • The survey covers the history of backpropagation within deep supervised learning.
  • Various approaches including evolutionary computation and indirect search are discussed.

Conclusions:

  • Deep artificial neural networks represent a significant advancement in machine learning.
  • Understanding credit assignment paths is key to designing effective deep learning models.
  • The historical context provides valuable insights into the evolution of deep learning techniques.