rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Deep Learning for Natural Language Processing
    sub title: Creating Neural Networks with Python
  name:
    name part: Goyal, Palash.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Pandey, Sumit.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Jain, Karan.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVII, 277 p. 99 illus., 2 illus. in color. online resource.
  abstract: Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You'll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. You will: Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification.
  table of contents: Chapter 1: Introduction to NLP and Deep Learning -- Chapter 2: Word Vector representations -- Chapter 3: Unfolding Recurrent Neural Networks -- Chapter 4: Developing a Chatbot -- Chapter 5: Research Paper Implementation: Sentiment Classification.
  note: by Palash Goyal, Sumit Pandey, Karan Jain.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Open source software
  subject:
    topic: Computer programming
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Python
  subject:
    topic: Open Source
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781484236857
  identifier: https://doi.org/10.1007/978-1-4842-3685-7
  record info:
    record creation date: 180626
    record change date: 20191019141350.0
    record identifier: 978-1-4842-3685-7