从基础到前沿，系统梳理AutoML关键技术与应用。
Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques
- 涵盖TPOT、AutoGluon等主流AutoML工具使用方法
- 解析神经网络架构搜索（NAS）等前沿技术原理
- 适合想快速掌握AutoML的初学者和进阶研究者
本文系统介绍了自动化机器学习（AutoML）的基础原理、实践方法与未来趋势。内容覆盖了初学者到资深从业者所需的知识，深入探讨了TPOT、AutoGluon、Auto-Keras等主流AutoML工具的实际应用。同时，文章还分析了神经架构搜索（NAS）等新兴方向在深度学习中的应用前景。本工作旨在推动人工智能与机器学习领域的持续研究与发展。
原文摘要 · Abstract (English)
A comprehensive guide to Automated Machine Learning (AutoML) is presented, covering fundamental principles, practical implementations, and future trends. The paper is structured to assist both beginners and experienced practitioners, with detailed discussions on popular AutoML tools such as TPOT, AutoGluon, and Auto-Keras. Emerging topics like Neural Architecture Search (NAS) and AutoML's applications in deep learning are also addressed. It is anticipated that this work will contribute to ongoing research and development in the field of AI and machine learning.
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