Introduction to deep learning using R : a step-by-step guide to learning and implementing deep learning models using R (Record no. 44554)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02333nam a22002177i 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781484227336 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 1484227336 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | BEY |
| 100 1# - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Beysolow, Taweh, II, |
| Relator term | Author |
| 245 10 - TITLE STATEMENT | |
| Title | Introduction to deep learning using R : a step-by-step guide to learning and implementing deep learning models using R |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | New York? : |
| Name of publisher | Apress, |
| Year of publication | ©2017. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xix, 227 pages : |
| Other physical details | illustrations ; |
| 490 1# - SERIES STATEMENT | |
| Series statement | For professionals by professionals |
| 500 ## - GENERAL NOTE | |
| General note | Includes index. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Introduction to deep learning -- Mathematical review -- A review of optimization and machine learning -- Single and multilayer perceptron models -- Convolutional neural networks (CNNs) -- Recurrent neural networks (RNNs) -- Autoencoders, restricted boltzmann machines, and deep belief networks -- Experimental design and heuristics -- Hardware and software suggestions -- Machine learning example problems -- Deep learning and other example problems -- Closing statements. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Big data. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | R (Computer program language) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Reference Books |
| Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Reference | Main Library | Main Library | Reference | 24/05/2019 | Purchased | 6590.00 | 006.31 BEY | 015850 | Reference Books |