Cracking the Machine Learning Code: Technicality or Innovation?
暫譯: 破解機器學習的密碼：技術性還是創新？

Santosh, Kc, Rizk, Rodrigue, Bajracharya, Siddhi K.

Cracking the Machine Learning Code: Technicality or Innovation?
  • 出版商: Springer
  • 出版日期: 2025-05-10
  • 售價: $5,730
  • 貴賓價: 9.5 折 $5,444
  • 語言: 英文
  • 頁數: 127
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9819727227
  • ISBN-13: 9789819727223
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

Employing off-the-shelf machine learning models is not an innovation. The journey through technicalities and innovation in the machine learning field is ongoing, and we hope this book serves as a compass, guiding the readers through the evolving landscape of artificial intelligence. It typically includes model selection, parameter tuning and optimization, use of pre-trained models and transfer learning, right use of limited data, model interpretability and explainability, feature engineering and autoML robustness and security, and computational cost - efficiency and scalability. Innovation in building machine learning models involves a continuous cycle of exploration, experimentation, and improvement, with a focus on pushing the boundaries of what is achievable while considering ethical implications and real-world applicability. The book is aimed at providing a clear guidance that one should not be limited to building pre-trained models to solve problems using the off-the-self basic building blocks. With primarily three different data types: numerical, textual, and image data, we offer practical applications such as predictive analysis for finance and housing, text mining from media/news, and abnormality screening for medical imaging informatics. To facilitate comprehension and reproducibility, authors offer GitHub source code encompassing fundamental components and advanced machine learning tools.

商品描述(中文翻譯)

使用現成的機器學習模型並不算創新。在機器學習領域，技術細節和創新的旅程仍在持續進行，我們希望這本書能作為一個指引，幫助讀者穿越不斷演變的人工智慧領域。這通常包括模型選擇、參數調整與優化、使用預訓練模型和遷移學習、有限數據的正確使用、模型的可解釋性和可解釋性、特徵工程、自動化機器學習的穩健性和安全性，以及計算成本的效率和可擴展性。構建機器學習模型的創新涉及探索、實驗和改進的持續循環，重點在於推動可實現的邊界，同時考慮倫理影響和現實世界的適用性。本書旨在提供清晰的指導，讓讀者不應僅限於使用現成的基本構建塊來構建預訓練模型以解決問題。我們主要處理三種不同的數據類型：數值、文本和圖像數據，並提供實用的應用案例，例如金融和房地產的預測分析、媒體/新聞的文本挖掘，以及醫學影像資訊的異常篩查。為了促進理解和可重現性，作者提供了涵蓋基本組件和先進機器學習工具的 GitHub 原始碼。

作者簡介

Prof. KC Santosh--a highly accomplished AI expert--is the chair of the Department of Computer Science and the founding director of the Applied AI Research Lab at the University of South Dakota. He is also served the National Institutes of Health as a research fellow and LORIA Research Center as a postdoctoral research scientist, in collaboration with industrial partner, ITESOFT, France. He earned his Ph.D. in Computer Science--Artificial Intelligence from INRIA Nancy Grand East Research Center (France). With funding exceeding $2 million from sources like DOD, NSF, and SDBOR, he has authored 10 books and over 250 peer-reviewed research articles, including IEEE TPAMI. He serves as an associate editor for esteemed journals such as IEEE Transactions on AI, Int. J of Machine Learning & Cybernetics, and Int. J of Pattern Recognition & Artificial Intelligence. He, founder of AI programs at USD, has significantly boosted graduate enrollment by over 3,000% in just three years, establishing USD as a leader in AI within South Dakota.
Dr. Rodrigue Rizk is an assistant professor at the University of South Dakota, holding a B.E. degree in computer and communication engineering with Summa Cum Laude highest honor distinction from Notre Dame University. He earned both his M.S. and Ph.D. degrees in Computer Engineering from the University of Louisiana at Lafayette, maintaining 4.0 GPA. Specializing in the dynamic interplay between software and hardware, his research interests span high-level computational systems, artificial intelligence, quantum computing, and more. He is a licensed professional engineer, a member of the Order of the Engineer, and holds various accolades, including the Richard G. and Mary B. Neiheisel endowed fellowship. He is a lifetime member of the Phi Kappa Phi honor society and a professional member of ACM and IEEE. His contributions have earned him numerous awards, including the President's Award for Educational Excellence and Outstanding Academic Achievement.
Mr. Siddhi K Bajracharya is a research fellow for the Applied AI Research Lab, Department of Computer Science at the University of South Dakota. His research study focuses on building generic and/or generalized machine learning models for multiple data types: numbers, texts, and images.

作者簡介(中文翻譯)

教授 KC Santosh 是一位成就卓越的人工智慧專家，擔任南達科他州大學計算機科學系主任及應用人工智慧研究實驗室創始主任。他曾在國家衛生研究院擔任研究員，並在法國 LORIA 研究中心擔任博士後研究科學家，與工業夥伴 ITESOFT 合作。他在法國的 INRIA Nancy Grand East 研究中心獲得計算機科學—人工智慧博士學位。獲得來自國防部 (DOD)、國家科學基金會 (NSF) 和南達科他州大學理事會 (SDBOR) 超過 200 萬美元的資助，他已撰寫 10 本書籍和超過 250 篇經過同行評審的研究文章，包括 IEEE TPAMI。他擔任多本知名期刊的副編輯，如 IEEE Transactions on AI、International Journal of Machine Learning & Cybernetics 和 International Journal of Pattern Recognition & Artificial Intelligence。他是南達科他州大學人工智慧計畫的創始人，在短短三年內使研究生入學人數增長超過 3000%，使南達科他州大學成為該州人工智慧領域的領導者。

羅德里克·瑞茲博士是南達科他州大學的助理教授，擁有聖母大學計算機與通信工程的榮譽學士學位 (Summa Cum Laude)。他在路易斯安那州立大學拉法葉校區獲得計算機工程的碩士和博士學位，並保持 4.0 的 GPA。他專注於軟體與硬體之間的動態互動，研究興趣涵蓋高階計算系統、人工智慧、量子計算等。他是一名持牌專業工程師，為工程師協會成員，並獲得多項榮譽，包括理查德·G·和瑪麗·B·奈海塞爾基金會獎學金。他是 Phi Kappa Phi 榮譽學會的終身會員，以及 ACM 和 IEEE 的專業會員。他的貢獻使他獲得多項獎項，包括總統教育卓越獎和傑出學術成就獎。

西迪·K·巴賴查里亞先生是南達科他州大學計算機科學系應用人工智慧研究實驗室的研究員。他的研究重點是為多種數據類型（數字、文本和圖像）構建通用和/或泛化的機器學習模型。

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