R Deep Learning Projects : (Record no. 1912)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 03270nam a2200253Ia 4500 |
| 001 - CONTROL NUMBER | |
| control field | 41622 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | IN-BdCUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230421153808.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230413s9999 000 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781788478403 |
| 040 ## - CATALOGING SOURCE | |
| Language of cataloging | eng |
| Transcribing agency | IN-BdCUP |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.502855133 |
| Item number | LIU |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Liu, Yuxi (Hayden) |
| 245 #0 - TITLE STATEMENT | |
| Title | R Deep Learning Projects : |
| Remainder of title | Master the Techniques to Design and Develop Neural Network Models in R / |
| Statement of responsibility, etc. | Liu, Yuxi (Hayden) & Maldonado, Pablo |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Burmingham : |
| Name of publisher, distributor, etc. | Packet Publishing, |
| Date of publication, distribution, etc. | 2018. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 258 p. ; |
| Dimensions | 24 cm. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 5 real-world projects to help you master deep learning concepts Key Features Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices Book Description R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R--including convolutional neural networks, recurrent neural networks, and LSTMs--and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages--such as MXNetR, H2O, deepnet, and more--to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting. What you will learn - Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec - Apply neural networks to perform handwritten digit recognition using MXNet - Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic sign classification -Implement credit card fraud detection with Autoencoders -Master reconstructing images using variational autoencoders - Wade through sentiment analysis from movie reviews - Run from past to future and vice versa with bidirectional Long Short-Term Memory (LSTM) networks - Understand the applications of Autoencoder Neural Networks in clustering and dimensionality reduction Who this book is for Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. A knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Computers |
| Topical term or geographic name entry element | R Deep Learning Projects |
| Topical term or geographic name entry element | Develop Neural Network |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Maldonado, Pablo |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Bill number | Total checkouts | Full call number | Barcode | Date last seen | Copy number | Actual Cost, replacement price | Bill Date | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Ranganathan Library | Ranganathan Library | 18/03/2020 | K. K. Distributors, Delhi | 899.00 | 4113 | 519.502855133 LIU | 038263 | 13/04/2023 | Copy 1 | 764.50 | 02/02/2020 | Book | |||||
| Dewey Decimal Classification | Ranganathan Library | Ranganathan Library | 18/03/2020 | K. K. Distributors, Delhi | 899.00 | 4113 | 519.502855133 LIU | 038264 | 13/04/2023 | Copy 2 | 764.50 | 02/02/2020 | Book |