Modern deep learning for tabular data: novel approaches to common modeling problems
Ye, Andre
Modern deep learning for tabular data: novel approaches to common modeling problems - New York Apress 2023 - xxviii, 842 p.
Table of contents:
Front Matter
Pages i-xxviii
Download chapter PDF
Machine Learning and Tabular Data
Front Matter
Pages 1-1
Download chapter PDF
Classical Machine Learning Principles and Methods
Andre Ye, Zian Wang
Pages 3-93
Data Preparation and Engineering
Andre Ye, Zian Wang
Pages 95-179
Applied Deep Learning Architectures
Front Matter
Pages 181-181
Download chapter PDF
Neural Networks and Tabular Data
Andre Ye, Zian Wang
Pages 183-258
Applying Convolutional Structures to Tabular Data
Andre Ye, Zian Wang
Pages 259-378
Applying Recurrent Structures to Tabular Data
Andre Ye, Zian Wang
Pages 379-450
Applying Attention to Tabular Data
Andre Ye, Zian Wang
Pages 451-548
Tree-Based Deep Learning Approaches
Andre Ye, Zian Wang
Pages 549-598
Deep Learning Design and Tools
Front Matter
Pages 599-599
Download chapter PDF
Autoencoders
Andre Ye, Zian Wang
Pages 601-680
Data Generation
Andre Ye, Zian Wang
Pages 681-710
Meta-optimization
Andre Ye, Zian Wang
Pages 711-752
Multi-model Arrangement
Andre Ye, Zian Wang
Pages 753-770
Neural Network Interpretability
Andre Ye, Zian Wang
Pages 771-792
[https://link.springer.com/book/10.1007/978-1-4842-8692-0]
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
(https://link.springer.com/book/10.1007/978-1-4842-8692-0)
9781484286913
Data science
Machine learning
006.31 / YE
Modern deep learning for tabular data: novel approaches to common modeling problems - New York Apress 2023 - xxviii, 842 p.
Table of contents:
Front Matter
Pages i-xxviii
Download chapter PDF
Machine Learning and Tabular Data
Front Matter
Pages 1-1
Download chapter PDF
Classical Machine Learning Principles and Methods
Andre Ye, Zian Wang
Pages 3-93
Data Preparation and Engineering
Andre Ye, Zian Wang
Pages 95-179
Applied Deep Learning Architectures
Front Matter
Pages 181-181
Download chapter PDF
Neural Networks and Tabular Data
Andre Ye, Zian Wang
Pages 183-258
Applying Convolutional Structures to Tabular Data
Andre Ye, Zian Wang
Pages 259-378
Applying Recurrent Structures to Tabular Data
Andre Ye, Zian Wang
Pages 379-450
Applying Attention to Tabular Data
Andre Ye, Zian Wang
Pages 451-548
Tree-Based Deep Learning Approaches
Andre Ye, Zian Wang
Pages 549-598
Deep Learning Design and Tools
Front Matter
Pages 599-599
Download chapter PDF
Autoencoders
Andre Ye, Zian Wang
Pages 601-680
Data Generation
Andre Ye, Zian Wang
Pages 681-710
Meta-optimization
Andre Ye, Zian Wang
Pages 711-752
Multi-model Arrangement
Andre Ye, Zian Wang
Pages 753-770
Neural Network Interpretability
Andre Ye, Zian Wang
Pages 771-792
[https://link.springer.com/book/10.1007/978-1-4842-8692-0]
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
(https://link.springer.com/book/10.1007/978-1-4842-8692-0)
9781484286913
Data science
Machine learning
006.31 / YE