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Deep Learning / Ian Goodfellow ; Yoshua Begio ; Aaron Courville.

Por: Colaborador(es): Series Adaptive computation and machine learningDetalles de publicación: Cambridge, MA : MIT Press, 2016Descripción: xxii, 775 pages : illustrationsTipo de contenido:
  • text
Tipo de medio:
  • unmediated
Tipo de soporte:
  • volume
ISBN:
  • 9780262035613
Materia(s): Clasificación:
  • 006.31
Contenidos:
Applied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models.
Copias
Tipo de ítem Biblioteca Colección Clasificación Copia Estado Código de barras
Libro Biblioteca Rancagua Colección General 006.31 G651d 2016 4 Disponible 35672004146
Libro Biblioteca Rancagua Colección General 006.31 G651d 2016 5 Disponible 35672010887
Libro Biblioteca Rancagua Colección General 006.31 G651d 2016 6 Disponible 35672010888
Libro Biblioteca Rancagua Colección General 006.31 G651d 2016 2 Disponible 35672003741
Libro Biblioteca Rancagua Colección General 006.31 G651d 2016 3 Disponible 35672003742
Libro Biblioteca Rancagua Colección General 006.31 G651d 2016 1 Disponible 005341

Includes bibliographical references and index.

Applied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models.

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