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

○ 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