http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R
XIX, 227 p. 106 illus., 53 illus. in color. online resource.
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.
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.-.
Programming languages (Electronic computers)
Programming Languages, Compilers, Interpreters