MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03106cam a2200337 i 4500 |
| 000 - LEADER |
|---|
| fixed length control field |
nam a22 7a 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
20789668 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20240128142828.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
181221s2019 maua b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2018059550 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9780262537551 |
| Qualifying information |
(pbk. ; |
| -- |
alk. paper) |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Transcribing agency |
DLC |
| Description conventions |
rda |
| Modifying agency |
DLC |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
pcc |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q325.5 |
| Item number |
.K454 2019 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.3/1 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Kelleher, John D., |
| Dates associated with a name |
1974- |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Deep learning / |
| Statement of responsibility, etc. |
John D. Kelleher. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Cambridge, Massachusetts : |
| Name of producer, publisher, distributor, manufacturer |
The MIT Press, |
| Date of production, publication, distribution, manufacture, or copyright notice |
[2019] |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
x, 280 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
18 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
unmediated |
| Media type code |
n |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
volume |
| Carrier type code |
nc |
| Source |
rdacarrier |
| 490 0# - SERIES STATEMENT |
|---|
| Series statement |
The MIT press essential knowledge series |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
"Artificial Intelligence is a disruptive technology across business and society. There are three long-term trends driving this AI revolution: the emergence of Big Data, the creation of cheaper and more powerful computers, and development of better algorithms for processing an learning from data. Deep learning is the subfield of Artificial Intelligence that focuses on creating large neural network models that are capable of making accurate data driven decisions. Modern neural networks are the most powerful computational models we have for analyzing massive and complex datasets, and consequently deep learning is ideally suited to take advantage of the rapid growth in Big Data and computational power. In the last ten years, deep learning has become the fundamental technology in computer vision systems, speech recognition on mobile phones, information retrieval systems, machine translation, game AI, and self-driving cars. It is set to have a massive impact in healthcare, finance, and smart cities over the next years. This book is designed to give an accessible and concise, but also comprehensive, introduction to the field of Deep Learning. The book explains what deep learning is, how the field has developed, what deep learning can do, and also discusses how the field is likely to develop in the next 10 years. Along the way, the most important neural network architectures are described, including autoencoders, recurrent neural networks, long short-term memory networks, convolutional networks, and more recent developments such as Generative Adversarial Networks, transformer networks, and capsule networks. The book also covers the two more important algorithms for training a neural network, the gradient descent algorithm and Backpropagation"-- |
| Assigning source |
Provided by publisher. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references (pages [261]-265) and index. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Artificial intelligence. |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| a |
7 |
| b |
cbc |
| c |
orignew |
| d |
1 |
| e |
ecip |
| f |
20 |
| g |
y-gencatlg |
| 925 0# - |
|---|
| -- |
acquire |
| -- |
1 shelf copy |
| -- |
policy default |
| 955 ## - COPY-LEVEL INFORMATION (RLIN) |
|---|
| -- |
rk09 2019-01-02 to Dewey |
| -- |
xm10 2019-01-02 |
| -- |
rl46 2020-03-19 (telework) to BCCD |
