國立虎尾科技大學 |

Introduction to Deep Learning = From Logical Calculus to Artificial Intelligence /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Introduction to Deep Learning/ by Sandro Skansi.
Reminder of title:	From Logical Calculus to Artificial Intelligence /
Author:	Skansi, Sandro.
Description:	XIII, 191 p. 38 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-319-73004-2
ISBN:	9783319730042

Introduction to Deep Learning = From Logical Calculus to Artificial Intelligence /
Skansi, Sandro.

Introduction to Deep LearningFrom Logical Calculus to Artificial Intelligence /[electronic resource] :by Sandro Skansi. - 1st ed. 2018. - XIII, 191 p. 38 illus.online resource. - Undergraduate Topics in Computer Science,1863-7310. - Undergraduate Topics in Computer Science,.

From Logic to Cognitive Science -- Mathematical and Computational Prerequisites -- Machine Learning Basics -- Feed-forward Neural Networks -- Modifications and Extensions to a Feed-forward Neural Network -- Convolutional Neural Networks -- Recurrent Neural Networks -- Autoencoders -- Neural Language Models -- An Overview of Different Neural Network Architectures -- Conclusion.

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. 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 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. Dr. Sandro Skansi is an Assistant Professor of Logic at the University of Zagreb and Lecturer in Data Science at University College Algebra, Zagreb, Croatia.

ISBN: 9783319730042

Standard No.: 10.1007/978-3-319-73004-2doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Introduction to Deep Learning = From Logical Calculus to Artificial Intelligence /
LDR:03492nam a22004095i 4500 001 997558
003 DE-He213
005 20200701001549.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319730042 $9 978-3-319-73004-2
024 7 $a 10.1007/978-3-319-73004-2 $2 doi
035 $a 978-3-319-73004-2
050 4 $a Q325.5-.7
050 4 $a TK7882.P3
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
100 1 $a Skansi, Sandro. $e author. $0 (orcid)0000-0002-3851-1186 $1 https://orcid.org/0000-0002-3851-1186 $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1288924
245 1 0 $a Introduction to Deep Learning $h [electronic resource] : $b From Logical Calculus to Artificial Intelligence / $c by Sandro Skansi.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XIII, 191 p. 38 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Undergraduate Topics in Computer Science, $x 1863-7310
505 0 $a From Logic to Cognitive Science -- Mathematical and Computational Prerequisites -- Machine Learning Basics -- Feed-forward Neural Networks -- Modifications and Extensions to a Feed-forward Neural Network -- Convolutional Neural Networks -- Recurrent Neural Networks -- Autoencoders -- Neural Language Models -- An Overview of Different Neural Network Architectures -- Conclusion.
520 $a 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. 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 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. Dr. Sandro Skansi is an Assistant Professor of Logic at the University of Zagreb and Lecturer in Data Science at University College Algebra, Zagreb, Croatia.
650 0 $a Machine learning. $3 561253
650 0 $a Pattern recognition. $3 1253525
650 0 $a Neural networks (Computer science) . $3 1253765
650 0 $a Coding theory. $3 561460
650 0 $a Information theory. $3 595305
650 1 4 $a Machine Learning. $3 1137723
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 884110
650 2 4 $a Coding and Information Theory. $3 669784
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319730035
776 0 8 $i Printed edition: $z 9783319730059
830 0 $a Undergraduate Topics in Computer Science, $x 1863-7310 $3 1254738
856 4 0 $u https://doi.org/10.1007/978-3-319-73004-2
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)