| 書(shū)目名稱 | Fundamental Mathematical Concepts for Machine Learning in Science | | 編輯 | Umberto Michelucci | | 視頻video | http://file.papertrans.cn/351/350003/350003.mp4 | | 概述 | Clearly explains the mathematical underpinnings essential for a robust understanding of machine learning algorithms.Coverage is tailored to students and researchers in all natural science areas, in ad | | 圖書(shū)封面 |  | | 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 de | | 出版日期 | Textbook 2024 | | 關(guān)鍵詞 | Machine Learning; Mathematics; Model Validation; Sampling Theory; Hyper-parameter Tuning; Linear Algebra | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-56431-4 | | isbn_softcover | 978-3-031-56433-8 | | isbn_ebook | 978-3-031-56431-4 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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