Introduction to Deep Learning Using R : A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R

Introduction to Deep Learning Using R A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R

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 Lea...

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Detalles Bibliográficos
Autor principal: Beysolow II, Taweh. author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Berkeley, CA : Apress 2017.
Edición:1st ed. 2017.
Materias:
Big data.
Artificial intelligence.
Programming languages (Electronic computers).
R (Computer program language).
Big Data/Analytics.
Artificial Intelligence.
Programming Languages, Compilers, Interpreters.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631370006719
Tabla de Contenidos:
  • 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.-.

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