Andre Ye, Zian Wang

Modern Deep Learning for Tabular Data (eBook, PDF)

Novel Approaches to Common Modeling Problems

• Format: PDF

Produktbeschreibung

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 powerof 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.

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Produktdetails

• Verlag: Apress
• Seitenzahl: 842
• Erscheinungstermin: 29. Dezember 2022
• Englisch
• ISBN-13: 9781484286920
• Artikelnr.: 67166021

Autorenporträt

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.

Inhaltsangabe

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.