Introduction to Deep Learning Using R : A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R / by Taweh Beysolow II.

Format:

Book

Author/Creator:

Beysolow II, Taweh., Author.

Language:

English

Subjects (All):

Big data.

Artificial intelligence.

Programming languages (Electronic computers).

R (Computer program language).

Big Data/Analytics.

Artificial Intelligence.

Programming Languages, Compilers, Interpreters.

Local Subjects:

Big Data/Analytics.

Artificial Intelligence.

Programming Languages, Compilers, Interpreters.

Physical Description:

1 online resource (XIX, 227 p. 106 illus., 53 illus. in color.)

Edition:

1st ed. 2017.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2017.

System Details:

text file

Summary:

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.

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.-.

Notes:

Includes index.

Includes bibliographical references.

ISBN:

9781484227343

1484227344

OCLC:

1077473905