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: Fundamentals of neural networks
  name:
    name part: Yin, Yiqiao
    role:
      role term: presenter.
  name:
    name part: Packt Publishing
    role:
      role term: publisher.
  type of resource: moving image
  genre: videorecording
  genre: Instructional films.
  genre: Nonfiction films.
  genre: Internet videos.
  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 (6 hr., 38 min.)) : sound, color.
  abstract: Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks (CNN), and (3) Recurrent Neural Networks (RNN). You will learn about logistic regression and linear regression and know the purpose of neural networks. You will also understand forward and backward propagation as well as the cross-entropy function. Furthermore, you will explore image data, convolutional operation, and residual networks. In the final section of the course, you will understand the use of RNN, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). You will also have code blocks and notebooks to help you understand the topics covered in the course. By the end of this course, you will have a hands-on understanding of Neural Networks in detail. What You Will Learn Learn about linear and logistic regression in ANN Learn about cross-entropy between two probability distributions Understand convolution operation which scans inputs with respect to their dimensions Understand VGG16, a convolutional neural network model Understand why to use recurrent neural network Understand Long short-term memory (LSTM) Audience This course can be taken by a beginner level audience that intends to obtain an in-depth overview of Artificial Intelligence, Deep Learning, and three major types of neural networks: Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. There is no prior codin…
  note: "Published in December 2022."
  note: Yiqiao Yin, presenter.
  subject:
    topic: Deep learning (Machine learning)
  classification: Q325.73
  classification: 006.3/1
  identifier: 9781837639519
  identifier: 1837639515
  identifier: 9781837639519 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: ORMDA
    record creation date: 230110
    record change date: 20260128155259.8
    record identifier: in00004673476
    language of cataloging:
      language term: eng