Probabilistic Graphical Model for Computer Vision

Unlock Pathway Codes, Reshape Cancer FightProfessor BENJAMIN YAT-MING YUNG, former Professor in the Department of Pharmacology at Chang Gung University College of Medicine, draws on forty years of accumulated research on NPM1/B23 to present the world's first network model of cancer drug resistance and recurrence pathways.This book breaks free from traditional therapy frameworks, delving into the four major characteristics of cancer to reveal the core pathogenesis of nucleolar imbalance and analyze the regulatory mechanisms of seven key pathway categories. The breakthrough QGCT® deconstructs personalized dysregulated pathways for patients, while providing pathway-based supplement applications and clinical solutions.More than just a popular science introduction to cancer knowledge, it offers patients new directions in precision treatment and provides scientific prevention strategies for health-conscious individuals. By integrating cutting-edge science with personalized care, it transform

這本書引導讀者從廣闊的人工智慧來到深度學習的核心概念，深化對機器學習的理解，並揭示電腦視覺如何利用機器學習來模仿和擴展人類的視覺能力。深度學習顯著提升了電腦視覺技術的效能，而成功的電腦視覺案例又進一步推進深度學習領域的發展，形成一個良性循環。　書中不僅提供扎實的理論基礎，更搭配豐富實用的範例，這些範例都是用強大且被廣泛運用的 Python 程式語言編寫。透過這些範例，讀者能更直觀地掌握書中所述的概念，並將理論付諸實踐。　本書特別為對影像處理與電腦視覺懷抱熱情的讀者而設計，無論是大學部或研究所的學生，或是渴望自學精進的其他族群，都能從中獲益良多。