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: Learn Keras for Deep Neural Networks
    sub title: A Fast-Track Approach to Modern Deep Learning with Python
  name:
    name part: Moolayil, Jojo.
    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: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 182 p. 37 illus. online resource.
  abstract: Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you'll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras. You will: Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks.
  table of contents: Chapter 1: Deep Learning & Keras -- Chapter 2: Keras in Action -- Chapter 3: Deep Neural networks for Supervised Learning -- Chapter 4: Measuring Performance for DNN -- Chapter 5: Hyperparameter Tuning & Model Deployment -- Chapter 6: The Path Forward.
  note: by Jojo Moolayil.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Open source software
  subject:
    topic: Computer programming
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Open Source
  subject:
    topic: Python
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781484242407
  identifier: https://doi.org/10.1007/978-1-4842-4240-7
  record info:
    record creation date: 181206
    record change date: 20190626101754.0
    record identifier: 978-1-4842-4240-7