<filename>1803-deeplearning/library.bib
@MISC{Suilin2017,
  title  = "1st Place Solution to Web Traffic Time Series Forecasting",
  author = "<NAME>",
  year   = 2017,
  url    = "https://www.kaggle.com/c/web-traffic-time-series-forecasting/discussion/43795"
}

@MISC{Karpathy2016-fy,
  title        = "The unreasonable effectiveness of recurrent neural networks",
  author       = "<NAME>",
  year         =  2016,
  url          = "karpathy.github.io/2015/05/21/rnn-effectiveness/"
}

@MISC{Olah2015-ez,
  title        = "Understanding {LSTM} Networks",
  booktitle    = "colah's blog",
  author       = "<NAME>",
  month        =  aug,
  year         =  2015,
  url          = "http://colah.github.io/posts/2015-08-Understanding-LSTMs/",
  note         = "Accessed: 2018-5-2"
}


@BOOK{Goodfellow2016-ma,
  title     = "Deep Learning",
  author    = "<NAME> and <NAME> and <NAME>",
  abstract  = "``Written by three experts in the field, Deep Learning is the
               only comprehensive book on the subject.'' -- Elon Musk, cochair
               of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is
               a form of machine learning that enables computers to learn from
               experience and understand the world in terms of a hierarchy of
               concepts. Because the computer gathers knowledge from
               experience, there is no need for a human computer operator to
               formally specify all the knowledge that the computer needs. The
               hierarchy of concepts allows the computer to learn complicated
               concepts by building them out of simpler ones; a graph of these
               hierarchies would be many layers deep. This book introduces a
               broad range of topics in deep learning. The text offers
               mathematical and conceptual background, covering relevant
               concepts in linear algebra, probability theory and information
               theory, numerical computation, and machine learning. It
               describes deep learning techniques used by practitioners in
               industry, including deep feedforward networks, regularization,
               optimization algorithms, convolutional networks, sequence
               modeling, and practical methodology; and it surveys such
               applications as natural language processing, speech recognition,
               computer vision, online recommendation systems, bioinformatics,
               and videogames. Finally, the book offers research perspectives,
               covering such theoretical topics as linear factor models,
               autoencoders, representation learning, structured probabilistic
               models, Monte Carlo methods, the partition function, approximate
               inference, and deep generative models. Deep Learning can be used
               by undergraduate or graduate students planning careers in either
               industry or research, and by software engineers who want to
               begin using deep learning in their products or platforms. A
               website offers supplementary material for both readers and
               instructors.",
  publisher = "MIT Press",
  month     =  nov,
  year      =  2016,
  language  = "en"
}
