Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R
| Author | : | |
| Rating | : | 4.68 (662 Votes) |
| Asin | : | 1484227336 |
| Format Type | : | paperback |
| Number of Pages | : | 227 Pages |
| Publish Date | : | 2014-04-04 |
| Language | : | English |
DESCRIPTION:
Taweh Beysolow II is a Machine Learning Scientist currently based in the United States with a passion for research and applying machine learning methods to solve problems. Currently, he is extremely passionate about all matters related to machine learning, data science, quantitative finance, and economics.. Johns University and a Master of Science in Applied Statistics from Fordham University. He has a Bachelor of Science degree in Economics from St
This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. From the Back CoverUnderstand 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. 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 practic
This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.What You'll LearnUnderstand the intuition and mathematics that power deep learning modelsUtilize various algorithms using the R programming language and its packagesUse best practices for experimental design and variable selectionPractice the methodology to approach and effectively solve problems as a data scientistEvaluate the effectiveness of algorithmic solutions and enhance their predictive powerWho This Book Is ForStuden