商品描述
This comprehensive textbook offers a unique approach to learning machine learning and deep learning by seamlessly integrating theoretical foundations with hands-on laboratory exercises. The book bridges the gap between theory and practice, providing students and practitioners with both the conceptual understanding and practical implementation skills necessary in today's AI landscape.
Each chapter follows a carefully structured format that combines theoretical explanations with immediate practical applications. This innovative approach allows readers to reinforce their understanding through direct implementation, creating a more engaging and effective learning experience.
商品描述(中文翻譯)
這本綜合性的教科書提供了一種獨特的學習機器學習和深度學習的方法，通過無縫整合理論基礎與實作實驗來實現。該書彌補了理論與實踐之間的鴻溝，為學生和從業者提供了在當今人工智慧（AI）環境中所需的概念理解和實際實施技能。
每一章都遵循精心結構化的格式，結合理論解釋與即時的實際應用。這種創新的方法使讀者能夠通過直接實施來加強他們的理解，創造出更具吸引力和有效的學習體驗。
作者簡介
Emilio Soria-Olivas received his B.Sc. degree (with honors) in Physics from the Universitat de València, Spain, in 1992, and his Ph.D. degree (cum laude) in Electronic Engineering in 1997. He is currently a Full Professor in the Electronic Engineering Department at the Universitat de València. His research focuses on the medical applications of advanced data analysis. Pablo Rodríguez Belenguer is a researcher in the field of medical imaging at the GIBI23⁰ group of the La Fe Health Research Institute. He received his PhD in Biomedicine in 2024, specializing in AI models applied to healthcare. His research interests include computer vision, deep learning, and reinforcement learning, with publications in top-tier journals and international conferences. He has collaborated on various interdisciplinary research initiatives (with research institutes, academia, and pharmaceutical companies) and is particularly focused on the clinical translation of AI systems and their integration into real-world healthcare environments. Diego Bonilla Salvador is a Deep Learning and Computer Vision researcher at Mango. Specialized and studied diffusion models and State of the Art Deep Learning architectures. He also has taught at several universities and master degrees as an instructor, like in University of Valencia. Jazz musician in his little free time. Emma Amorós Belda is a Machine Learning Engineer at Openbank and a Ph.D. candidate in Artificial Intelligence at the University of Valencia. With a background in Physics and Data Science, her work spans predictive modelling, anomaly detection, and specializes in developing AI models and cloud-based solutions. She collaborates with the Intelligent Data Analysis Laboratory (IDAL) and teaches in the University of Valencia's Master in Data Science program. Pablo Hernández-Cámara is a postdoctoral researcher at the Image Processing Laboratory of the Universitat de València. He received his PhD in Electronic Engineering in 2025, specializing in perceptual alignment between human vision and deep neural networks. His research interests include computer vision, deep learning, and bio-inspired models of perception, with publications in leading journals and conferences. He has participated in multiple national and international research projects and regularly contributes to academic teaching and scientific outreach. Manuel Sánchez-Montañés received his B.Sc. degree (with honors) in Physics from the Universidad Complutense de Madrid, Spain, 1997, and Ph.D. degree (cum laude) in Computer Science from the Universidad Autónoma de Madrid, Spain, 2003. He is currently part of the permanent faculty of the Computer Science Department, Universidad Autónoma de Madrid. His research activity is focused in Artificial Intelligence and Advanced Data Analysis, carrying out theoretical developments and applications.
作者簡介(中文翻譯)
Emilio Soria-Olivas於1992年在西班牙瓦倫西亞大學獲得物理學學士學位（榮譽），並於1997年獲得電子工程博士學位（優等）。他目前是瓦倫西亞大學電子工程系的正教授。他的研究專注於先進數據分析在醫療應用中的應用。
Pablo Rodríguez Belenguer是La Fe健康研究所GIBI23⁴小組的醫學影像研究員。他於2024年獲得生物醫學博士學位，專攻應用於醫療保健的人工智慧模型。他的研究興趣包括計算機視覺、深度學習和強化學習，並在頂尖期刊和國際會議上發表過多篇論文。他參與了多個跨學科研究計畫（與研究機構、學術界和製藥公司合作），特別專注於人工智慧系統的臨床轉化及其在現實醫療環境中的整合。
Diego Bonilla Salvador是Mango的深度學習和計算機視覺研究員，專注於擴散模型和最先進的深度學習架構。他還在多所大學和碩士課程中擔任講師，例如瓦倫西亞大學。在他有限的空閒時間裡，他是一名爵士音樂家。
Emma Amorós Belda是Openbank的機器學習工程師，也是瓦倫西亞大學人工智慧的博士候選人。她擁有物理學和數據科學的背景，工作範圍涵蓋預測建模、異常檢測，並專注於開發人工智慧模型和基於雲的解決方案。她與智能數據分析實驗室（IDAL）合作，並在瓦倫西亞大學的數據科學碩士課程中授課。
Pablo Hernández-Cámara是瓦倫西亞大學影像處理實驗室的博士後研究員。他於2025年獲得電子工程博士學位，專攻人類視覺與深度神經網絡之間的感知對齊。他的研究興趣包括計算機視覺、深度學習和生物啟發的感知模型，並在領先的期刊和會議上發表過多篇論文。他參與了多個國內外研究項目，並定期貢獻於學術教學和科學普及。
Manuel Sánchez-Montañés於1997年在西班牙馬德里康普頓斯大學獲得物理學學士學位（榮譽），並於2003年在馬德里自治大學獲得計算機科學博士學位（優等）。他目前是馬德里自治大學計算機科學系的專任教員。他的研究活動專注於人工智慧和先進數據分析，進行理論發展和應用。
