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. What You Will Learn Important concepts and
developments in modern machine learning and deep learning, with a
strong emphasis on tabular data applications. Understand the
promising links between deep learning and tabular data, and when a
deep learning approach is or isn't appropriate. Apply promising
research and unique modeling approaches in real-world data
contexts. Explore and engage with modern, research-backed
theoretical advances on deep tabular modeling Utilize unique and
successful preprocessing methods to prepare tabular data for
successful modelling. Who This Book Is ForData scientists and
researchers of all levels from beginner to advanced looking to
level up results on tabular data with deep learning or to
understand the theoretical and practical aspects of deep tabular
modeling research. Applicable to readers seeking to apply deep
learning to all sorts of complex tabular data contexts, including
business, finance, medicine, education, and security.
General
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