Image from OpenLibrary

Deep Learning / Ian Goodfellow, Yoshua Bengio, Aaron Courville

By: Contributor(s): Material type: TextTextPublication details: Estados Unidos de América : MIT Press, 2016Description: 775 p.; 23,5 x18 cmISBN:
  • 978-0-262-03561-3 (Pasta dura)
Subject(s): DDC classification:
  • 006.31 G651d 2016
Contents:
Applied math and machine learning basics. -- Linear algebra. -- Probability and information theory. -- Numerical Computation. -- Machine learning basics. -- Deep netwoks: modern practices. -- Regularization for deep learning. -- Optimization for training deep models. -- Convolutional Networks. -- Sequuence modeling: recurrent and recursive nets. -- Practical methodology. -- Applications. -- Deep learning research. -- Lineal factor models. -- Autoencoders. -- Representation learning. -- Structured probabilistic models for deep learning. -- Monte carlo methods. -- Confronting the partition fuction. -- Aproximate interference. -- Deep Generative models.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Collection Call number Status Date due Barcode
Libro Libro Unicomfacauca Acervo general de Libros Available T05468

Incluye índice

Applied math and machine learning basics. -- Linear algebra. -- Probability and information theory. -- Numerical Computation. -- Machine learning basics. -- Deep netwoks: modern practices. -- Regularization for deep learning. -- Optimization for training deep models. -- Convolutional Networks. -- Sequuence modeling: recurrent and recursive nets. -- Practical methodology. -- Applications. -- Deep learning research. -- Lineal factor models. -- Autoencoders. -- Representation learning. -- Structured probabilistic models for deep learning. -- Monte carlo methods. -- Confronting the partition fuction. -- Aproximate interference. -- Deep Generative models.

There are no comments on this title.

to post a comment.