Fundamental Mathematical Concepts for Machine Learning in Science

Michelucci, Umberto

Fundamental Mathematical Concepts for Machine Learning in Science
  • 出版商: Springer
  • 出版日期: 2024-05-17
  • 售價: $3,120
  • 貴賓價: 9.5 折 $2,964
  • 語言: 英文
  • 頁數: 249
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031564308
  • ISBN-13: 9783031564307
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

This book is for individuals with a scientific background who aspire to apply machine learning within various natural science disciplines--such as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous mathematical integrity. For readers more versed in mathematics, the book includes advanced sections that are not prerequisites for the initial reading. It ensures concepts are clearly defined and theorems are proven where it's pertinent. Machine learning transcends the mere implementation and training of algorithms; it encompasses the broader challenges of constructing robust datasets, model validation, addressing imbalanced datasets, and fine-tuning hyperparameters. These topics are thoroughly examined within the text, along with the theoretical foundations underlying these methods. Rather than concentrating on particular algorithms this book focuses on the comprehensive concepts and theories essential for their application. It stands as an indispensable resource for any scientist keen on integrating machine learning effectively into their research.

Numerous texts delve into the technical execution of machine learning algorithms, often overlooking the foundational concepts vital for fully grasping these methods. This leads to a gap in using these algorithms effectively across diverse disciplines. For instance, a firm grasp of calculus is imperative to comprehend the training processes of algorithms and neural networks, while linear algebra is essential for the application and efficient training of various algorithms, including neural networks. Absent a solid mathematical base, machine learning applications may be, at best, cursory, or at worst, fundamentally flawed.

商品描述(中文翻譯)

本書適合具有科學背景的個人，旨在將機器學習應用於各種自然科學領域，如物理、化學、生物、醫學、心理學等。它以易於理解且直接的方式闡明核心數學概念，同時保持嚴謹的數學完整性。對於數學基礎較為扎實的讀者，本書還包含一些進階章節，這些章節並不是初讀的前提條件。書中確保概念清晰定義，並在相關處證明定理。機器學習不僅僅是算法的實施和訓練；它還涵蓋了構建穩健數據集、模型驗證、處理不平衡數據集以及微調超參數等更廣泛的挑戰。這些主題在文本中得到了徹底的探討，並且探討了這些方法背後的理論基礎。本書不專注於特定的算法，而是聚焦於其應用所需的綜合概念和理論。對於任何希望有效將機器學習整合到其研究中的科學家來說，這本書都是不可或缺的資源。

許多文本深入探討機器學習算法的技術執行，卻常常忽略了充分理解這些方法所需的基礎概念。這導致在不同學科中有效使用這些算法的差距。例如，對微積分的深刻理解對於理解算法和神經網絡的訓練過程至關重要，而線性代數則對於各種算法（包括神經網絡）的應用和高效訓練是必不可少的。缺乏堅實的數學基礎，機器學習應用可能充其量只是表面，或在最糟的情況下根本存在缺陷。

作者簡介

Umberto Michelucci has a PhD in Machine Learning and Physics from the University of Portsmouth. He is the cofounder and Chief AI scientist of TOELT LLC, a company aiming to develop new and modern teaching, coaching, and research methods for AI to make AI technologies and research accessible to every company and everyone. He's an expert in numerical simulation, statistics, data science, and machine learning. In addition to several years of research experience at the George Washington University (USA) and the University of Augsburg (DE), he has 15 years of practical experience in the fields of data warehouse, data science, and machine learning. His first book, Applied Deep Learning--A Case-Based Approach to Understanding Deep Neural Networks, was published by Apress in 2018. He followed with Convolutional and Recurrent Neural Networks Theory and Applications in 2019. He's very active in research in the field of artificial intelligence. He publishes his research results regularly in leading journals and gives regular talks at international conferences. Umberto studied physics and mathematics. Sharing is caring--for that, he is a lecturer at the ZHAW University of Applied Sciences for deep learning and neural networks theory and applications. He's also responsible at Helsana Versicherung AG for research and collaborations with universities in the area of AI. He is also a Google Developer Expert in Machine Learning based in Switzerland.

作者簡介(中文翻譯)

Umberto Michelucci 擁有英國朴茨茅斯大學的機器學習和物理學博士學位。他是 TOELT LLC 的共同創辦人及首席 AI 科學家，該公司旨在開發新的現代教學、輔導和研究方法，使 AI 技術和研究對每家公司和每個人都能夠獲得。他在數值模擬、統計學、數據科學和機器學習方面是專家。除了在喬治華盛頓大學（美國）和奧格斯堡大學（德國）擁有數年的研究經驗外，他在數據倉儲、數據科學和機器學習領域也有 15 年的實務經驗。他的第一本書《Applied Deep Learning--A Case-Based Approach to Understanding Deep Neural Networks》於 2018 年由 Apress 出版。隨後，他在 2019 年出版了《Convolutional and Recurrent Neural Networks Theory and Applications》。他在人工智慧領域的研究非常活躍，定期在領先的期刊上發表研究成果，並在國際會議上進行演講。Umberto 學習了物理學和數學。他相信分享就是關懷，因此他在 ZHAW 應用科學大學擔任深度學習和神經網絡理論及應用的講師。他還在 Helsana Versicherung AG 負責 AI 領域的研究和與大學的合作。他也是位於瑞士的 Google Developer Expert in Machine Learning。

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