Modern Deep Learning for Tabular Data: Novel Approaches to Common Modeling Problems
暫譯: 現代深度學習於表格數據:針對常見建模問題的新穎方法
Ye, Andre, Wang, Andy
- 出版商: Apress
- 出版日期: 2022-12-30
- 售價: $2,320
- 貴賓價: 9.5 折 $2,204
- 語言: 英文
- 頁數: 842
- 裝訂: Quality Paper - also called trade paper
- ISBN: 148428691X
- ISBN-13: 9781484286913
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相關分類:
DeepLearning
海外代購書籍(需單獨結帳)
商品描述
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.
商品描述(中文翻譯)
深度學習是現代人工智慧領域中最強大的工具之一。雖然它主要應用於高度專業化的影像、文本和信號數據集,但本書綜合並呈現了針對一個看似不太可能的領域——表格數據的新穎深度學習方法。無論是在金融、商業、安全、醫療或無數其他領域,深度學習都能幫助挖掘和建模表格數據中的複雜模式——這是一種極為普遍的結構化數據形式。
本書的第一部分提供了與全面建模和操作表格數據相關的機器學習原則、算法和實施技能的嚴謹概述。第二部分研究了五種主流的深度學習模型設計——人工神經網絡(Artificial Neural Networks)、卷積神經網絡(Convolutional Neural Networks)、遞歸神經網絡(Recurrent Neural Networks)、注意力機制和變壓器(Attention and Transformers)、以及樹根網絡(Tree-Rooted Networks)——通過它們的「默認」用法及其在表格數據中的應用。第三部分通過調查策略和技術來增強深度學習系統的能力,進一步擴展了前面所涵蓋的方法:自編碼器(autoencoders)、深度數據生成(deep data generation)、元優化(meta-optimization)、多模型排列(multi-model arrangement)和神經網絡可解釋性(neural network interpretability)。每一章都配有豐富的可視化、代碼和相關研究的覆蓋。
《現代深度學習與表格數據》是同類書籍中的首創之一——廣泛探索深度學習理論及其在表格數據中的應用,整合並記錄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。
您將學到什麼
- 現代機器學習和深度學習中的重要概念和發展,特別強調表格數據的應用。
- 理解深度學習與表格數據之間的有希望的聯繫,以及何時適合或不適合採用深度學習方法。
- 在現實數據情境中應用有前景的研究和獨特的建模方法。
- 探索並參與現代、基於研究的深度表格建模理論進展。
- 利用獨特且成功的預處理方法,為成功建模準備表格數據。
本書適合誰
本書適合所有級別的數據科學家和研究人員,從初學者到高級專業人士,尋求利用深度學習提升表格數據的結果,或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、醫療、教育和安全等領域。
作者簡介
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.作者簡介(中文翻譯)
Andre Ye 是一位深度學習研究員,專注於為不確定、模糊和不尋常的情境構建和訓練穩健的醫療深度電腦視覺系統。他與 Apress 共同出版了另一本書,名為 Modern Deep Learning Design and Applications,並在他的部落格上撰寫短篇數據科學文章。在空閒時間,Andre 喜歡跟進當前的深度學習研究並享受重金屬音樂。
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。他在現代 AI 工具和應用方面擁有豐富的經驗,並參加過各種專業數據科學競賽,同時在他發表的文章中獲得了數百到數千的瀏覽量。他的主要焦點在於為不同問題設定構建多功能的模型管道,包括表格數據和電腦視覺相關任務。在不寫作或編程的時候,Andy 對鋼琴和游泳也充滿熱情。