Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques
This is an incremental work that serves as a tutorial or review for AI and machine learning practitioners, without introducing new methods or results.
The paper provides a comprehensive guide to Automated Machine Learning (AutoML), covering fundamentals, tools like TPOT and AutoGluon, and emerging topics such as Neural Architecture Search, aimed at assisting practitioners and contributing to AI research.
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.