東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Artificial intelligence for engineers = basics and implementations /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial intelligence for engineers/ by Zhen "Leo" Liu.
其他題名:	basics and implementations /
作者:	Liu, Zhen.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xiii, 439 p. :ill. (chiefly color), digital ;24 cm.
內容註:	Preparation Knowledge: Basics of AI -- Tools for Artificial Intelligence -- Linear Models -- Decision Trees -- Support Vector Machine -- Bayesian Algorithms -- Artificial Neural Network -- Deep Learning -- Ensemble Learning -- Clustering -- Dimension Reduction -- Anomaly Detection -- Association Rule Leaming -- Basics of and Value-Based Reinforcement Learning -- Policy-Based Reinforcement Learning.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-031-75953-6
ISBN:	9783031759536

Artificial intelligence for engineers = basics and implementations /
Liu, Zhen.

Artificial intelligence for engineersbasics and implementations /[electronic resource] :by Zhen "Leo" Liu. - Cham :Springer Nature Switzerland :2025. - xiii, 439 p. :ill. (chiefly color), digital ;24 cm.

Preparation Knowledge: Basics of AI -- Tools for Artificial Intelligence -- Linear Models -- Decision Trees -- Support Vector Machine -- Bayesian Algorithms -- Artificial Neural Network -- Deep Learning -- Ensemble Learning -- Clustering -- Dimension Reduction -- Anomaly Detection -- Association Rule Leaming -- Basics of and Value-Based Reinforcement Learning -- Policy-Based Reinforcement Learning.

This textbook presents basic knowledge and essential toolsets needed for people who want to step into artificial intelligence (AI). The book is especially suitable for those college students, graduate students, instructors, and IT hobbyists who have an engineering mindset. That is, it serves the idea of getting the job done quickly and neatly with an adequate understanding of why and how. It is designed to allow one to obtain a big picture for both AI and essential AI topics within the shortest amount of time. Designed for a typical undergraduate, graduate, or dual-listed course with a semester-based calendar; Puts theory in context, so readers gain knowledge about the most essential concepts and algorithms; Covers essential terms, algorithms, and useful tools for learning and performing contemporary AI. Extra information is available at AI-engineer.org.

ISBN: 9783031759536

Standard No.: 10.1007/978-3-031-75953-6doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: TA347.A78

Dewey Class. No.: 670.28563

Artificial intelligence for engineers = basics and implementations /
LDR:02262nmm a2200325 a 4500 001 2408394
003 DE-He213
005 20250104115228.0
006 m o d
007 cr nn 008maaau
008 260204s2025 sz s 0 eng d
020 $a 9783031759536 $q (electronic bk.)
020 $a 9783031759529 $q (paper)
024 7 $a 10.1007/978-3-031-75953-6 $2 doi
035 $a 978-3-031-75953-6
040 $a GP $c GP
041 0 $a eng
050 4 $a TA347.A78
072 7 $a TJFC $2 bicssc
072 7 $a TEC008010 $2 bisacsh
072 7 $a TJFC $2 thema
082 0 4 $a 670.28563 $2 23
090 $a TA347.A78 $b L783 2025
100 1 $a Liu, Zhen. $3 900571
245 1 0 $a Artificial intelligence for engineers $h [electronic resource] : $b basics and implementations / $c by Zhen "Leo" Liu.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xiii, 439 p. : $b ill. (chiefly color), digital ; $c 24 cm.
505 0 $a Preparation Knowledge: Basics of AI -- Tools for Artificial Intelligence -- Linear Models -- Decision Trees -- Support Vector Machine -- Bayesian Algorithms -- Artificial Neural Network -- Deep Learning -- Ensemble Learning -- Clustering -- Dimension Reduction -- Anomaly Detection -- Association Rule Leaming -- Basics of and Value-Based Reinforcement Learning -- Policy-Based Reinforcement Learning.
520 $a This textbook presents basic knowledge and essential toolsets needed for people who want to step into artificial intelligence (AI). The book is especially suitable for those college students, graduate students, instructors, and IT hobbyists who have an engineering mindset. That is, it serves the idea of getting the job done quickly and neatly with an adequate understanding of why and how. It is designed to allow one to obtain a big picture for both AI and essential AI topics within the shortest amount of time. Designed for a typical undergraduate, graduate, or dual-listed course with a semester-based calendar; Puts theory in context, so readers gain knowledge about the most essential concepts and algorithms; Covers essential terms, algorithms, and useful tools for learning and performing contemporary AI. Extra information is available at AI-engineer.org.
650 0 $a Artificial intelligence. $3 516317
650 1 4 $a Electronics Design and Verification. $3 3592716
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Machine Learning. $3 3382522
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-75953-6
950 $a Engineering (SpringerNature-11647)