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Modern deep learning for tabular datanovel 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 databy 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.
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$q(electronic bk.)

Modern deep learning for tabular datanovel 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$q(electronic bk.)

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

188639
Machine learning.

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

Dewey Class. No.: 006.31

Modern deep learning for tabular datanovel approaches to common modeling problems /
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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.
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