Description
Book SynopsisDeep 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 pow
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
