Mathematical Engineering of Deep Learning

Liquet, Benoit, Moka, Sarat, Nazarathy, Yoni

Mathematical Engineering of Deep Learning
  • 出版商: CRC
  • 出版日期: 2024-10-03
  • 售價: $2,930
  • 貴賓價: 9.5 折 $2,784
  • 語言: 英文
  • 頁數: 402
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1032288280
  • ISBN-13: 9781032288284
  • 相關分類: DeepLearning

    • 海外代購書籍(需單獨結帳)

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商品描述

Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics. The book provides a self-contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning. These ideas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement learning, and graph neural networks. Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade. The content is the foundation for state-of-the-art artificial intelligence applications, involving images, sound, large language models, and other domains. The focus is on the basic mathematical description of algorithms and methods and does not require computer programming. The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep learning.

Key Features:

  • A perfect summary of deep learning not tied to any computer language, or computational framework.
  • An ideal handbook of deep learning for readers that feel comfortable with mathematical notation.
  • An up-to-date description of the most influential deep learning ideas that have made an impact on vision, sound, natural language understanding, and scientific domains.
  • The exposition is not tied to the historical development of the field or to neuroscience, allowing the reader to quickly grasp the essentials.

Deep learning is easily described through the language of mathematics at a level accessible to many professionals. Readers from fields such as engineering, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, quantitative biology, applied machine learning, or applied deep learning will quickly gain insights into the key mathematical engineering components of the field.

商品描述(中文翻譯)

《深度學習的數學工程》提供了使用數學語言對深度學習的完整而簡明的概述。本書提供了機器學習和優化算法的自足背景，並逐步介紹深度學習的關鍵概念。這些概念和架構包括深度神經網絡、卷積模型、遞迴模型、長短期記憶、注意力機制、變壓器、變分自編碼器、擴散模型、生成對抗網絡、強化學習和圖神經網絡。概念以簡單的數學方程式呈現，並附有相關技巧的簡明描述。內容是最先進的人工智慧應用的基礎，涉及圖像、聲音、大型語言模型和其他領域。重點在於算法和方法的基本數學描述，並不需要計算機編程。這種呈現方式也不依賴於神經科學關係、歷史觀點和理論研究。這種簡明的方法的好處在於，具備數學基礎的讀者可以迅速掌握深度學習的本質。

主要特點：
- 對深度學習的完美總結，與任何計算機語言或計算框架無關。
- 對於熟悉數學符號的讀者來說，是一本理想的深度學習手冊。
- 對於在視覺、聲音、自然語言理解和科學領域產生影響的最具影響力的深度學習思想的最新描述。
- 說明不依賴於該領域的歷史發展或神經科學，使讀者能夠迅速掌握要點。

深度學習可以通過數學語言輕鬆描述，且其水平對許多專業人士來說是可接觸的。來自工程、統計、物理、純數學、計量經濟學、運籌學、定量管理、定量生物學、應用機器學習或應用深度學習等領域的讀者將迅速獲得對該領域關鍵數學工程組件的洞察。

作者簡介

Dr. Benoit Liquet is a Professor of Mathematical and Computational Statistics at Macquarie University currently on detachment from his professor position at Université de Pau et Pays de l'Adour (France). He also holds an adjunct position at The University of Queensland. His research spans the broad spectrum of applied statistics, with a focus on statistical modeling for complex data. He has made significant contributions to methodological developments, exploiting modern statistical and computational cutting-edge methods to tackle a variety of real-world problems from small, designed studies to large-scale high-dimensional data challenges in bioinformatics and biometrics. His research extends to the development of R packages and industrial applications, particularly in the realm of machine learning. Over the years, he has authored numerous articles, book chapters, and books, including the co-authored book "The R Software, Fundamentals of Programming and Statistical Analysis". He has also co-authored books on dynamical biostatistical models and developed over a dozen R packages, making his methodologies accessible to a wide range of users. He is deeply committed to education and has taught advanced courses in statistics and machine learning at multiple institutions around the globe. Such educational activities reflect his dedication to bridging the gap between theoretical advancements and practical applications.

Dr. Sarat Moka is an academic researcher and educator at the School of Mathematics and Statistics at The University of New South Wales (UNSW). His research interests encompass applied probability, computational statistics, machine learning, and deep learning. Dr. Moka has made contributions to optimization methods for efficient model selection in high-dimensional settings. Additionally, he has developed fast unbiased sampling and estimation techniques for spatial point processes and random graphs. Moreover, his research focus extends to efficient pruning methods for deep neural networks. In addition to research, he has been actively teaching advanced statistical and deep learning courses. Prior to joining UNSW in 2023, he was a senior research fellow at the School of Mathematical and Physical Science at Macquarie University and held an ACEMS (ARC Centre of Excellence for Mathematical & Statistical Frontiers) postdoctoral researcher position in the School of Mathematics and Physics at The University of Queensland. He earned a PhD in Applied Probability from the School of Technology and Computer Science at Tata Institute of Fundamental Research, and a Master's and a Bachelor's in Engineering with a focus on electrical, electronics, and communications, at the Indian Institute of Science and Andhra University, respectively. Before pursuing his doctoral studies, he was a scientist at the Indian Space Research Organization (SHAR, Sriharikota), where he worked on Communication Networks that support rocket launch activities.

Dr. Yoni Nazarathy is an Associate Professor at the School of Mathematics and Physics at The University of Queensland (UQ). He is also a consultant and co-director of a machine learning consultancy, Accumulation Point. His research spans applied probability, statistics, and machine learning and his industry work includes biostatistical software development, data science training for industry, and large language models. In addition to his many refereed publications in the mathematical sciences, he is a co-author of the book "Statistics with Julia". Prior to his PhD in the field of queueing theory at the University of Haifa, Israel, he worked as a software engineer, algorithm developer, and team leader in the Israeli tech industry. He then followed with post doc positions in The Netherlands, and academic positions in Melbourne, Australia, before settling at UQ where he has been for over a decade. He is also an avid educator and has taught and created academic and professional courses across the spectrum. He has also contributed to mathematical education via software apps and engagement with pre-university level educators.

作者簡介(中文翻譯)

Dr. Benoit Liquet 是麥考瑞大學數學與計算統計的教授，目前從法國的波城大學（Université de Pau et Pays de l'Adour）暫時調派。他同時在昆士蘭大學擔任兼任職位。他的研究涵蓋應用統計的廣泛範疇，專注於複雜數據的統計建模。他在方法論發展方面做出了重要貢獻，利用現代統計和計算的尖端方法來解決各種現實世界的問題，從小型設計研究到生物信息學和生物識別中的大規模高維數據挑戰。他的研究還延伸到 R 套件的開發和工業應用，特別是在機器學習領域。多年來，他撰寫了大量文章、書籍章節和書籍，包括合著的書籍《The R Software, Fundamentals of Programming and Statistical Analysis》。他還合著了有關動態生物統計模型的書籍，並開發了十多個 R 套件，使他的研究方法對廣泛的用戶可及。他對教育充滿熱情，在全球多個機構教授高級統計和機器學習課程。這些教育活動反映了他在理論進展與實際應用之間架起橋樑的承諾。

Dr. Sarat Moka 是新南威爾士大學（UNSW）數學與統計學院的學術研究者和教育者。他的研究興趣包括應用概率、計算統計、機器學習和深度學習。Moka 博士在高維環境中為高效模型選擇做出了優化方法的貢獻。此外，他還開發了針對空間點過程和隨機圖的快速無偏抽樣和估計技術。此外，他的研究重點還擴展到深度神經網絡的高效剪枝方法。除了研究，他還積極教授高級統計和深度學習課程。在 2023 年加入 UNSW 之前，他是麥考瑞大學數學與物理科學學院的高級研究員，並在昆士蘭大學數學與物理學院擔任 ACEMS（ARC 數學與統計前沿卓越中心）博士後研究員。他在塔塔基礎研究所的技術與計算機科學學院獲得應用概率的博士學位，並在印度科學研究所和安得拉大學分別獲得電氣、電子和通信工程的碩士和學士學位。在攻讀博士學位之前，他曾在印度空間研究組織（SHAR, Sriharikota）擔任科學家，負責支持火箭發射活動的通信網絡工作。

Dr. Yoni Nazarathy 是昆士蘭大學（UQ）數學與物理學院的副教授。他同時也是一家名為 Accumulation Point 的機器學習顧問公司的顧問和共同主任。他的研究涵蓋應用概率、統計和機器學習，並且他的行業工作包括生物統計軟件開發、行業數據科學培訓和大型語言模型。除了在數學科學領域的多篇同行評審出版物外，他還是書籍《Statistics with Julia》的合著者。在以色列海法大學獲得排隊理論博士學位之前，他曾在以色列科技產業擔任軟件工程師、算法開發者和團隊領導。隨後，他在荷蘭從事博士後研究，並在澳大利亞墨爾本擔任學術職位，最終定居於昆士蘭大學，至今已超過十年。他也是一位熱衷的教育者，教授並創建了學術和專業課程。

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