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

Modern Deep Learning for Tabular Data: Novel Approaches to Common Modeling Problems – Ebook Instant Download/Delivery ISBN(s): 9781484286920,9781484286913,1484286928,148428691X,3998949136

Product details:

• ISBN-10 ‏ : ‎ 148428691X
• ISBN-13 ‏ : ‎ 978-1484286913
• Author(s): 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.

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 power of 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.

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

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