Modern Deep Learning for Tabular Data: Novel Approaches to Common Modeling Problems

Author:   Andre Ye ,  Zian Wang
Publisher:   APress
Edition:   1st ed.
ISBN:  

9781484286913


Pages:   842
Publication Date:   30 December 2022
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Modern Deep Learning for Tabular Data: Novel Approaches to Common Modeling Problems


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Full Product Details

Author:   Andre Ye ,  Zian Wang
Publisher:   APress
Imprint:   APress
Edition:   1st ed.
Weight:   1.628kg
ISBN:  

9781484286913


ISBN 10:   148428691
Pages:   842
Publication Date:   30 December 2022
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

○      Section 1: Machine Learning and Tabular Data ■      Chapter 1 – Introduction to Machine Learning ■      Chapter 2 – Data Tools ○      Section 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 ○      Section 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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Author Information

Andre Ye is a deep learning researcher with a focus on building and training robust medical deep computer vision systems for uncertain, ambiguous, and unusual contexts. He has published another book with Apress, Modern Deep Learning Design and Applications, and writes short-form data science articles on his blog. In his spare time, Andre enjoys keeping up with current deep learning research and jamming to hard metal.  Andy Wang is a researcher and technical writer passionate about data science and machine learning. With extensive experiences in modern AI tools and applications, he has competed in various professional data science competitions while gaining hundreds and thousands of views across his published articles. His main focus lies in building versatile model pipelines for different problem settings including tabular and computer-vision related tasks. At other times while Andy is not writing or programming, he has a passion for piano and swimming.

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