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

Pohsun Feng, Ziqian Bi, Yizhu Wen, Benji Peng, Junyu Liu +11 more
10/12/2024
cs.LG

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Abstract

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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Cite this paper

@article{feng2024mastering,
  title  = {Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques},
  author = {Pohsun Feng and Ziqian Bi and Yizhu Wen and Benji Peng and Junyu Liu and Caitlyn Heqi Yin and Tianyang Wang and Keyu Chen and Sen Zhang and Ming Li and Jiawei Xu and Ming Liu and Xuanhe Pan and Jinlang Wang and Xinyuan Song and Qian Niu},
  year   = {2024},
  eprint = {2410.09596},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.09596v3}
}

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