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: Introduction to transformer models for NLP
    sub title: using BERT, GPT, and more to solve modern natural language processing tasks
  name:
    name part: Ozdemir, Sinan
    role:
      role term: presenter.
  name:
    name part: Addison-Wesley Professional (Firm)
    role:
      role term: publisher.
  type of resource: moving image
  genre: videorecording
  genre: Instructional films.
  genre: Internet videos.
  genre: Nonfiction films.
  origin info:
    place:
      place term: xx
    date issued: 2022
    edition: [First edition].
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (1 video file (10 hr., 14 min.)) : sound, color.
  abstract: 10+ Hours of Video Instruction Learn how to apply state-of-the-art transformer-based models including BERT and GPT to solve modern NLP tasks. Overview Introduction to Transformer Models for NLP LiveLessons provides a comprehensive overview of transformers and the mechanisms--attention, embedding, and tokenization--that set the stage for state-of-the-art NLP models like BERT and GPT to flourish. The focus for these lessons is providing a practical, comprehensive, and functional understanding of transformer architectures and how they are used to create modern NLP pipelines. Throughout this series, instructor Sinan Ozdemir will bring theory to life through illustrations, solved mathematical examples, and straightforward Python examples within Jupyter notebooks. All lessons in the course are grounded by real-life case studies and hands-on code examples. After completing this lesson, you will be in a great position to understand and build cutting-edge NLP pipelines using transformers. You will also be provided with extensive resources and curriculum detail which can all be found at the course's GitHub repository. About the Instructor Sinan Ozdemir'is currently Founder and CTO of Shiba Technologies. Sinan is a former lecturer of Data Science at Johns Hopkins University and the author of multiple textbooks on data science and machine learning. Additionally, he is the founder of the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a master's degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco, CA. Skill Level Intermediate Advanced Learn How To Recognize which type of transformer-based model is best for a given task Understand how transformers process text and make predictions Fine-tune a transformer-based model Create pipelines using fine-tuned models Deploy fine-tuned models and use them in production Who Should Take This Course Intermediate/advanced machine learning engineers with…
  note: Sinan Ozdemir, presenter.
  subject:
    topic: Natural language processing (Computer science)
  subject:
    topic: Natural language processing (Computer science)
  classification: QA76.9.N38
  classification: 006.3/5
  related item:
    title info:
      title: LiveLessons (Boston, Massachusetts)
  identifier: 9780137923717
  identifier: 0137923716
  identifier: 9780137923717 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: ORMDA
    record creation date: 220830
    record change date: 20260128204953.8
    record identifier: in00004672116
    language of cataloging:
      language term: eng