Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques

• URL: http://arxiv.org/abs/2410.09596v1
• Date: Sat, 12 Oct 2024 17:11:39 GMT
• Title: Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques
• Authors: Pohsun Feng, Ziqian Bi, Yizhu Wen, Benji Peng, Junyu Liu, Caitlyn Heqi Yin, Tianyang Wang, Keyu Chen, Sen Zhang, Ming Li, Jiawei Xu, Ming Liu, Xuanhe Pan, Jinlang Wang, Qian Niu,
• Abstract summary: The paper is structured to assist both beginners and experienced practitioners, with detailed discussions on popular AutoML tools. It also addresses emerging topics like Neural Architecture Search (NAS) and AutoML's applications in deep learning.
• Score: 17.62426370778165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This manuscript presents a comprehensive guide to Automated Machine Learning (AutoML), 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. It also addresses emerging topics like Neural Architecture Search (NAS) and AutoML's applications in deep learning. We believe this work will contribute to ongoing research and development in the field of AI and machine learning.

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