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

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.

深度學習是現代人工智慧領域中最強大的工具之一。儘管它主要應用於高度專門化的圖像、文本和信號數據集，但本書將新穎的深度學習方法綜合並呈現給一個看似不太可能的領域 - 表格數據。無論是金融、商業、安全、醫學還是無數其他領域，深度學習都可以幫助挖掘和建模表格數據中的複雜模式 - 這是一種非常普遍的結構化數據形式。

本書的第一部分提供了機器學習原理、算法和實現技巧的嚴謹概述，這些對於全面建模和操作表格數據非常重要。第二部分通過對五種主要的深度學習模型設計 - 人工神經網絡、卷積神經網絡、循環神經網絡、注意力和轉換器以及樹根網絡 - 的研究，介紹了它們在“默認”用法和應用於表格數據時的情況。第三部分通過調查超級加速深度學習系統的策略和技術，進一步提升了之前介紹的方法的能力：自編碼器、深度數據生成、元優化、多模型佈局和神經網絡可解釋性。每一章都附有豐富的可視化、代碼和相關研究報導。

《現代深度學習應用於表格數據》是其類型中的首批之一 - 對深度學習理論和應用於表格數據的廣泛探索，整合並記錄了該領域的新方法和技術。本書提供了一個強大的概念和理論工具包，以應對具有挑戰性的表格數據問題。

你將學到什麼：
- 現代機器學習和深度學習中的重要概念和發展，強調表格數據應用。
- 理解深度學習與表格數據之間的有希望的聯繫，以及何時使用深度學習方法是否適合。
- 在實際數據情境中應用有前景的研究和獨特的建模方法。
- 探索並參與深度表格建模的現代、研究支持的理論進展。
- 利用獨特且成功的預處理方法，為成功建模準備表格數據。

適合閱讀對象：
從初學者到高級的數據科學家和研究人員，希望通過深度學習提升表格數據的結果，或者理解深度表格建模研究的理論和實踐方面。適用於希望將深度學習應用於各種複雜表格數據情境的讀者，包括商業、金融、醫學、教育和安全等領域。

Andre Ye is a deep learning researcher with a focus on building and training robust medical deep computer vision systems for uncertain, ambiguous, and unusual contexts. He has published another book with Apress, Modern Deep Learning Design and Applications, and writes short-form data science articles on his blog. In his spare time, Andre enjoys keeping up with current deep learning research and jamming to hard metal.

Andy Wang is a researcher and technical writer passionate about data science and machine learning. With extensive experiences in modern AI tools and applications, he has competed in various professional data science competitions while gaining hundreds and thousands of views across his published articles. His main focus lies in building versatile model pipelines for different problem settings including tabular and computer-vision related tasks. At other times while Andy is not writing or programming, he has a passion for piano and swimming.

Andre Ye 是一位深度學習研究員，專注於建立和訓練穩健的醫學深度電腦視覺系統，以應對不確定、模糊和不尋常的情境。他在Apress出版了另一本書，《現代深度學習設計與應用》，並在他的博客上撰寫了短篇的數據科學文章。在閒暇時間，Andre喜歡跟上當前的深度學習研究並聆聽重金屬音樂。

Andy Wang 是一位研究員和技術作家，對數據科學和機器學習充滿熱情。憑藉對現代人工智慧工具和應用的廣泛經驗，他參加了各種專業數據科學競賽，並在他發表的文章中獲得了數百萬的觀看次數。他的主要關注點是為不同的問題設定建立多功能的模型流程，包括表格和計算機視覺相關任務。當Andy不寫作或編程時，他熱愛彈鋼琴和游泳。