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

Modern deep learning for tabular dat...

Ye, Andre.

FindBook

Google Book

Amazon

博客來

Modern deep learning for tabular data = novel approaches to common modeling problems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modern deep learning for tabular data/ by Andre Ye, Zian Wang.
其他題名:	novel approaches to common modeling problems /
作者:	Ye, Andre.
其他作者:	Wang, Zian.
出版者:	Berkeley, CA :Apress : : 2023.,
面頁冊數:	xxviii, 842 p. :ill., digital ;24 cm.
內容註:	Part 1: Machine Learning and Tabular Data -- Chapter 1 - Introduction to Machine Learning -- Chapter 2 - Data Tools -- Part 2: Applied Deep Learning Architectures -- Chapter 3 - Artificial Neural Networks -- Chapter 4 - Convolutional Neural Networks -- Chapter 5 - Recurrent Neural Networks -- Chapter 6 - Attention Mechanism -- Chapter 7 - Tree-based Neural Networks -- Part 3: Deep Learning Design and Tools -- Chapter 8 - Autoencoders -- Chapter 9 - Data Generation -- Chapter 10 - Meta-optimization -- Chapter 11 - Multi-model arrangement -- Chapter 12 - Deep Learning Interpretability -- Appendix A.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-1-4842-8692-0
ISBN:	9781484286920

Modern deep learning for tabular data = novel approaches to common modeling problems /
Ye, Andre.

Modern deep learning for tabular datanovel approaches to common modeling problems /[electronic resource] :by Andre Ye, Zian Wang. - Berkeley, CA :Apress :2023. - xxviii, 842 p. :ill., digital ;24 cm.

Part 1: Machine Learning and Tabular Data -- Chapter 1 - Introduction to Machine Learning -- Chapter 2 - Data Tools -- Part 2: Applied Deep Learning Architectures -- Chapter 3 - Artificial Neural Networks -- Chapter 4 - Convolutional Neural Networks -- Chapter 5 - Recurrent Neural Networks -- Chapter 6 - Attention Mechanism -- Chapter 7 - Tree-based Neural Networks -- Part 3: Deep Learning Design and Tools -- Chapter 8 - Autoencoders -- Chapter 9 - Data Generation -- Chapter 10 - Meta-optimization -- Chapter 11 - Multi-model arrangement -- Chapter 12 - Deep Learning Interpretability -- Appendix A.

Deep learning is one of the most powerful tools in the modern artificial intelligence landscape. While having been predominantly applied to highly specialized image, text, and signal datasets, this book synthesizes and presents novel deep learning approaches to a seemingly unlikely domain - tabular data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data - an incredibly ubiquitous form of structured data. Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs - Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks - through both their 'default' usage and their application to tabular data. Part III compounds the power of the previously covered methods by surveying strategies and techniques to supercharge deep learning systems: autoencoders, deep data generation, meta-optimization, multi-model arrangement, and neural network interpretability. Each chapter comes with extensive visualization, code, and relevant research coverage. Modern Deep Learning for Tabular Data is one of the first of its kind - a wide exploration of deep learning theory and applications to tabular data, integrating and documenting novel methods and techniques in the field. This book provides a strong conceptual and theoretical toolkit to approach challenging tabular data problems. You will: Gain insight into important concepts and developments in modern machine learning and deep learning, with a strong emphasis on tabular data applications. Understand the promising links between deep learning and tabular data, and when a deep learning approach is or isn't appropriate. Apply promising research and unique modeling approaches in real-world data contexts. Explore and engage with modern, research-backed theoretical advances on deep tabular modeling Utilize unique and successful preprocessing methods to prepare tabular data for successful modelling.

ISBN: 9781484286920

Standard No.: 10.1007/978-1-4842-8692-0doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .Y42 2023

Dewey Class. No.: 006.31

Modern deep learning for tabular data = novel approaches to common modeling problems /
LDR:03849nmm a2200325 a 4500 001 2315237
003 DE-He213
005 20221229072656.0
006 m d
007 cr nn 008mamaa
008 230902s2023 cau s 0 eng d
020 $a 9781484286920 $q (electronic bk.)
020 $a 9781484286913 $q (paper)
024 7 $a 10.1007/978-1-4842-8692-0 $2 doi
035 $a 978-1-4842-8692-0
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .Y42 2023
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .Y37 2023
100 1 $a Ye, Andre. $3 3593812
245 1 0 $a Modern deep learning for tabular data $h [electronic resource] : $b novel approaches to common modeling problems / $c by Andre Ye, Zian Wang.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2023.
300 $a xxviii, 842 p. : $b ill., digital ; $c 24 cm.
505 0 $a Part 1: Machine Learning and Tabular Data -- Chapter 1 - Introduction to Machine Learning -- Chapter 2 - Data Tools -- Part 2: Applied Deep Learning Architectures -- Chapter 3 - Artificial Neural Networks -- Chapter 4 - Convolutional Neural Networks -- Chapter 5 - Recurrent Neural Networks -- Chapter 6 - Attention Mechanism -- Chapter 7 - Tree-based Neural Networks -- Part 3: Deep Learning Design and Tools -- Chapter 8 - Autoencoders -- Chapter 9 - Data Generation -- Chapter 10 - Meta-optimization -- Chapter 11 - Multi-model arrangement -- Chapter 12 - Deep Learning Interpretability -- Appendix A.
520 $a Deep learning is one of the most powerful tools in the modern artificial intelligence landscape. While having been predominantly applied to highly specialized image, text, and signal datasets, this book synthesizes and presents novel deep learning approaches to a seemingly unlikely domain - tabular data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data - an incredibly ubiquitous form of structured data. Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs - Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks - through both their 'default' usage and their application to tabular data. Part III compounds the power of the previously covered methods by surveying strategies and techniques to supercharge deep learning systems: autoencoders, deep data generation, meta-optimization, multi-model arrangement, and neural network interpretability. Each chapter comes with extensive visualization, code, and relevant research coverage. Modern Deep Learning for Tabular Data is one of the first of its kind - a wide exploration of deep learning theory and applications to tabular data, integrating and documenting novel methods and techniques in the field. This book provides a strong conceptual and theoretical toolkit to approach challenging tabular data problems. You will: Gain insight into important concepts and developments in modern machine learning and deep learning, with a strong emphasis on tabular data applications. Understand the promising links between deep learning and tabular data, and when a deep learning approach is or isn't appropriate. Apply promising research and unique modeling approaches in real-world data contexts. Explore and engage with modern, research-backed theoretical advances on deep tabular modeling Utilize unique and successful preprocessing methods to prepare tabular data for successful modelling.
650 0 $a Machine learning. $3 533906
650 0 $a Mathematical models. $3 522882
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Science. $3 3538937
700 1 $a Wang, Zian. $3 3627432
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-8692-0
950 $a Professional and Applied Computing (SpringerNature-12059)