MARC details
000 -LEADER |
fixed length control field |
02450nam a22002537a 4500 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
fixed length control field |
230201s2018 |||||||| |||| 00| 0 eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
International Standard Book Number |
9783319730035 |
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
ISSN-L |
9783319730035 |
041 ## - LANGUAGE CODE |
Language code of text/sound track or separate title |
English |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
Classification number |
006.31 |
Item number |
SKA |
100 ## - MAIN ENTRY--PERSONAL NAME |
Personal name |
Skansi, Sandro |
9 (RLIN) |
673678 |
Relator term |
author |
245 ## - TITLE STATEMENT |
Title |
Introduction to Deep Learning from Logical Calculus to Artificial Intelligence |
260 ## - PUBLICATION, DISTRIBUTION, ETC. |
Place of publication, distribution, etc. |
Cham, Switzerland : |
Name of publisher, distributor, etc. |
Springer, |
Date of publication, distribution, etc. |
c2018 |
300 ## - PHYSICAL DESCRIPTION |
Extent |
XIII, 191 p. |
Other physical details |
: ill |
490 ## - SERIES STATEMENT |
Series statement |
Undergraduate Topics in Computer Science |
504 ## - BIBLIOGRAPHY, ETC. NOTE |
Bibliography, etc. note |
Includes Bibliographical References and Index |
520 ## - SUMMARY, ETC. |
Summary, etc. |
SUMMARY:<br/><br/><br/>This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.<br/>Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.<br/><br/>This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.<br/> |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
Coding Theory |
9 (RLIN) |
987 |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
9 (RLIN) |
156028 |
Topical term or geographic name entry element |
Information Theory |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
Machine Learning |
9 (RLIN) |
845 |
856 ## - ELECTRONIC LOCATION AND ACCESS |
Link text |
WEB LINK |
Uniform Resource Identifier |
<a href="https://link.springer.com/book/10.1007/978-3-319-73004-2#toc ">https://link.springer.com/book/10.1007/978-3-319-73004-2#toc </a> |
856 ## - ELECTRONIC LOCATION AND ACCESS |
Link text |
TOC |
Uniform Resource Identifier |
<a href="https://eaklibrary.neduet.edu.pk:8443/catalog/bk/books/toc/9783319730035.pdf">https://eaklibrary.neduet.edu.pk:8443/catalog/bk/books/toc/9783319730035.pdf</a> |
942 ## - ADDED ENTRY ELEMENTS (KOHA) |
Source of classification or shelving scheme |
Dewey Decimal Classification |
Koha item type |
Book |