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: End-to-end deep learning for predicting Airbnb prices
  name:
    name part: Ryan, Mark
    role:
      role term: presenter.
  name:
    name part: Manning (Firm)
    role:
      role term: publisher.
  type of resource: moving image
  genre: videorecording
  genre: Webcast
  genre: Instructional films.
  genre: Internet videos.
  genre: Nonfiction films.
  genre: Films de formation.
  genre: Films autres que de fiction.
  genre: Vidéos sur Internet.
  origin info:
    place:
      place term: xx
    date issued: 2020
    edition: First edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (1 video file (45 min.)) : sound, color.
  abstract: Learn how to bring the power of deep learning to your structured, tabular data. Go hands-on with an open dataset detailing Airbnb rentals in New York City, and take on the challenge of creating an end-to-end deep learning solution for predicting prices. Each segment revolves around an essential task of the deep learning pipeline, and is a great way to get started applying deep learning to real-world problems.
  note: Mark Ryan.
  note: Mark Ryan, presenter.
  subject:
    topic: Deep learning (Machine learning)
  subject:
    topic: Deep learning (Machine learning)
  classification: Q325.73
  classification: 006.3/1
  identifier: 10000MNLV202152 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: ORMDA
    record creation date: 220307
    record change date: 20260129212749.0
    record identifier: in00004670519
    language of cataloging:
      language term: eng