@MASTERSTHESIS\{IMM2016-06930,
    author       = "E. S. Lynge",
    title        = "Recurrent neural networks for language modeling",
    year         = "2016",
    school       = "Technical University of Denmark, Department of Applied Mathematics and Computer Science",
    address      = "Richard Petersens Plads, Building 324, {DK-}2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk",
    type         = "",
    note         = "Supervisor: Ole Winther, olwi@dtu.dk, {DTU} Compute",
    url          = "http://www.compute.dtu.dk/English.aspx",
    abstract     = "The goal of the thesis is to explore the mechanisms and tools that enables efficient development of Recurrent Neural Networks, how to train them and what they can accomplish in regard to character level language modelling. Specifically Gated Recurrence Units and Long Short Term Memory are the focal point of the training and language modelling. Choice of data sets, hyper parameters and visualization methods, aims to reproduce parts of [KJL15]. More broadly {RNN} as a concept is explored through computational graphs and back propagation. Several concrete software tools written in python 3 is developed as part of the project, and discussed briefly in the thesis."
}