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

Deep learning is a powerful tool for analyzing tabular data, and this book provides a comprehensive guide to implementing deep learning models for various domains. It covers machine learning principles, model designs, and techniques to enhance deep learning systems, with extensive visualization and code.

Format: Paperback / softback
Length: 842 pages
Publication date: 30 December 2022
Publisher: APress

Deep learning is a revolutionary tool in the realm of artificial intelligence, offering immense potential for analyzing and manipulating complex data. While it has primarily been applied to specialized image, text, and signal datasets, this book takes a bold step by exploring the application of deep learning to tabular data, a ubiquitous and structured form of information. Whether in finance, business, security, medicine, or countless other domains, deep learning has the power to uncover hidden patterns and insights within tabular data, a task that was once considered daunting.

In Part I of the book, we delve into the fundamental principles of machine learning and their application to tabular data modeling. We cover a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning, with a focus on developing effective algorithms for tabular data analysis. We also explore the use of neural networks, a powerful class of machine learning models, for tabular data representation and classification.

Part II of the book takes a deeper dive into five dominant deep learning model designs: Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks. We examine these models through their default usage and their application to tabular data, highlighting their strengths and limitations. We also discuss the techniques used to optimize these models for tabular data, such as data augmentation, regularization, and batch normalization.

Part III of the book takes the exploration of deep learning to the next level by surveying strategies and techniques to supercharge deep learning systems. We discuss autoencoders, a technique used for data compression and dimensionality reduction; deep data generation, a method for generating synthetic data to improve model performance; meta-optimization, a technique for optimizing the hyperparameters of deep learning models; multi-model arrangement, a technique for combining multiple models to enhance performance; and neural network interpretability, a technique for understanding the decision-making process of deep learning models.

Each chapter in the book is accompanied by extensive visualization, code, and relevant research coverage, making it an invaluable resource for both practitioners and researchers in the field of deep learning. Whether you are a beginner or an experienced practitioner, Modern Deep Learning for Tabular Data provides a comprehensive and up-to-date guide to approaching challenging tabular data problems.

In conclusion, deep learning has the potential to transform the way we analyze and interpret data, and this book offers a comprehensive and practical guide to harnessing its power for tabular data analysis. By integrating and documenting novel methods and techniques, Modern Deep Learning for Tabular Data provides a strong conceptual and theoretical toolkit to approach complex tabular data problems. So, whether you are working in finance, business, security, or any other domain, this book is a must-read for anyone seeking to leverage the power of deep learning to unlock the hidden insights within structured data.

Weight: 1605g
Dimension: 254 x 178 (mm)
ISBN-13: 9781484286913
Edition number: 1st ed.