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Deep Learning / Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

By: Contributor(s): Material type: TextLanguage: English Series: Publisher: Cambridge, Massachusetts ; London : The MIT Press, 2016Description: xvii, 775 páginas (algunas a color) : ilustraciones ; 24 centímetrosContent type:
  • texto
Media type:
  • sin mediación
Carrier type:
  • volumen
ISBN:
  • 9780262035613
Subject(s): DDC classification:
  • 21 006.3 G651d
Partial contents:
Applied Math and machine learning basics -- Linear algebra -- Probability and information theory -- Numerical computation -- Mchine learning basics -- Deep networks: modern practices -- Deep feedforward networks -- Regularization for deep lerning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research: Linear factors models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Mote Carlo methods -- Confronting the partition Function -- Appoximate Inference -- Deep Generative models .
Abstract: Una introducción a una amplia gama de temas en el aprendizaje profundo, que cubre antecedentes matemáticos y conceptuales, técnicas de aprendizaje profundo utilizadas en la industria y perspectivas de investigación.
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Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
LIBROS - MATERIAL GENERAL BIBLIOTECA CENTRAL General 006.3 G651d (Browse shelf(Opens below)) Ej.: 1 Available 085920
Total holds: 0

Incluye bibliografía e índice.

Applied Math and machine learning basics -- Linear algebra -- Probability and information theory -- Numerical computation -- Mchine learning basics -- Deep networks: modern practices -- Deep feedforward networks -- Regularization for deep lerning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research: Linear factors models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Mote Carlo methods -- Confronting the partition Function -- Appoximate Inference -- Deep Generative models .

Una introducción a una amplia gama de temas en el aprendizaje profundo, que cubre antecedentes matemáticos y conceptuales, técnicas de aprendizaje profundo utilizadas en la industria y perspectivas de investigación.

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