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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Beysolow II, Taweh (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berkeley, CA : Apress : Imprint: Apress, 2017.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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.-.