Modern Deep Learning for Tabular Data: Novel Approaches to Common Modeling Problems 1st Edition

Modern Deep Learning for Tabular Data: Novel Approaches to Common Modeling Problems 1st Edition – Ebook Instant Download/Delivery ISBN(s): 9781484286913,148428691X,9781484286920, 1484286928

Product details:

• ISBN-10: 1484286928
• ISBN-13: 9781484286920
• Author: Andre Ye; Zian Wang

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.

Table contents:

Part I. Machine Learning and Tabular Data
1. Classical Machine Learning Principles and Methods
2. Data Preparation and Engineering
Part II. Applied Deep Learning Architectures
3. Neural Networks and Tabular Data
4. Applying Convolutional Structures to Tabular Data
5. Applying Recurrent Structures to Tabular Data
6. Applying Attention to Tabular Data
7. Tree-Based Deep Learning Approaches
Part III. Deep Learning Design and Tools
8. Autoencoders
9. Data Generation
10. Meta-optimization
11. Multi-model Arrangement
12. Neural Network Interpretability

People also search:

types of tabular data

deep learning models for tabular data

deep learning for tabular data

deep learning for tabular data using pytorch

data cube vs tabular model