دانلود کتاب مقدمه‌ای بر سیستم‌های یادگیری ماشین – اصول و روش‌های مهندسی سیستم‌های هوشمند مصنوعی

The goal of this book is to provide a resource for educators and learners seeking to understand the principles and practices of machine learning systems. This book is continually updated to incorporate the latest insights and effective teaching strategies with the intent that it remains a valuable resource in this fast-evolving field. So please check back often! The book originated as a collaborative effort with contributions from students, researchers, and practitioners. While maintaining its academic rigor and realworld applicability, it continues to evolve through regular updates and careful curation to reflect the latest developments in machine learning systems. This textbook follows a carefully designed pedagogical progression that mirrors how expert ML systems engineers actually develop their skills. The learning journey unfolds in five distinct phases: Phase 1: Theory – Build your conceptual foundation through Foundations and Design Principles, establishing the mental models that underpin all effective systems work. Phase 2: Performance – Master Performance Engineering to transform theoretical understanding into systems that run efficiently in resource-constrained real-world environments. Phase 3: Practice – Navigate Robust Deployment challenges, learning how to make systems work reliably beyond the controlled environment of development. Phase 4: Ethics – Explore Trustworthy Systems to ensure your systems serve society beneficially and sustainably. Phase 5: Vision – Look toward ML Systems Frontiers to understand emerging paradigms and prepare for the next generation of challenges.