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 : deep neural network for beginners using Python
  name:
    name part: AI Sciences (Firm)
    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: 2023
    edition: [First edition].
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (1 video file (6 hr., 28 min.)) : sound, color.
  abstract: Are you ready to start your path to becoming a deep learning expert? Then this course is for you. This course is step-by-step. In every new tutorial, we build on what we have already learned and move one extra step forward, and then we assign you a small task that is solved at the beginning of the next video. We start by teaching the theoretical part of the concept, and then implement everything as it is practically using Python. This comprehensive course will be your guide to learning how to use the power of Python to train your machine such that your machine starts learning just like humans, and based on that learning, your machine starts making predictions as well! We will be using Python as a programming language in this course, which is the hottest language nowadays if we talk about machine learning. Python will be taught from the elementary level up to an advanced level so that any machine learning concept can be implemented. You will also learn various steps of data preprocessing, which allows us to make data ready for machine learning algorithms. You will learn the general concepts of machine learning overall, which will be followed by the implementation of one of the essential ML algorithms, "Deep Neural Networks". Each concept of DNNs will be taught theoretically and will be implemented using Python. By the end of this course, you will be able to understand the methodology of DNNs with deep learning using real-world datasets. What You Will Learn Learn the basics of machine learning and neural networks Understand the architecture of neural networks Learn the basics of training a DNN using the Gradient Descent algorithm Learn how to implement a complete DNN using NumPy Learn to create a complete structure for DNN from scratch using Python Work on a project using deep learning for the IRIS dataset Audience This course is designed for anyone who is interested in data science or interested in taking their data-speak to a higher level. Students who want to maste…
  note: "Published in January 2023."
  note: AI Sciences, presenter.
  subject:
    topic: Deep learning (Machine learning)
  subject:
    topic: Neural networks (Computer science)
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Computer programming
  classification: Q325.73
  classification: 006.3/1
  identifier: 9781837633579
  identifier: 1837633576
  identifier: 9781837633579 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: ORMDA
    record creation date: 230220
    record change date: 20260129212551.7
    record identifier: in00004673725
    language of cataloging:
      language term: eng