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

• Main Author: Beysolow II, Taweh (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berkeley, CA : Apress : Imprint: Apress, 2017.
• Subjects: Business.; Big data.; Programming languages (Electronic computers).; Computers.; Business and Management.; Big Data/Analytics.; Computing Methodologies.; Programming Languages, Compilers, Interpreters.

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...

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.-.