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 Deep Learning Using R
    sub title: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R
  name:
    name part: Beysolow II, Taweh.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIX, 227 p. 106 illus., 53 illus. in color. online resource.
  abstract: Understand deep learning, the nuances of its different models, and where these models can be applied. The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools. What You Will Learn: • Understand the intuition and mathematics that power deep learning models • Utilize various algorithms using the R programming language and its packages • Use best practices for experimental design and variable selection • Practice the methodology to approach and effectively solve problems as a data scientist • Evaluate the effectiveness of algorithmic solutions and enhance their predictive power.
  table of contents: Chapter 1: What is Deep Learning? -- Chapter 2: Mathematical Review -- Chapter 3: A Review of Optimization and Machine Learning -- Chapter 4: Single and Multi-Layer Perceptron Models -- Chapter 5: Convolutional Neural Networks (CNNs) -- Chapter 6: Recurrent Neural Networks (RNNs) -- Chapter 7: Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks -- Chapter 8: Experimental Design and Heuristics -- Chapter 9: Deep Learning and Machine Learning Hardware/Software Suggestions -- Chapter 10: Machine Learning Example Problems -- Chapter 11: Deep Learning and Other Example Problems -- Chapter 12: Closing Statements.-.
  note: by Taweh Beysolow II.
  subject:
    topic: Business
  subject:
    topic: Big data
  subject:
    topic: Programming languages (Electronic computers)
  subject:
    topic: Computers
  subject:
    topic: Business and Management
  subject:
    topic: Big Data/Analytics
  subject:
    topic: Computing Methodologies
  subject:
    topic: Programming Languages, Compilers, Interpreters
  classification: HF5548.125-HF5548.6
  classification: 658.4038
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781484227343
  identifier: http://dx.doi.org/10.1007/978-1-4842-2734-3
  record info:
    record creation date: 170720
    record change date: 20170720081451.0
    record identifier: 978-1-4842-2734-3