Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915658267099136 |
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| author | Feng, Pohsun Bi, Ziqian Wen, Yizhu Peng, Benji Liu, Junyu Yin, Caitlyn Heqi Wang, Tianyang Chen, Keyu Zhang, Sen Li, Ming Xu, Jiawei Liu, Ming Pan, Xuanhe Wang, Jinlang Song, Xinyuan Niu, Qian |
| author_facet | Feng, Pohsun Bi, Ziqian Wen, Yizhu Peng, Benji Liu, Junyu Yin, Caitlyn Heqi Wang, Tianyang Chen, Keyu Zhang, Sen Li, Ming Xu, Jiawei Liu, Ming Pan, Xuanhe Wang, Jinlang Song, Xinyuan Niu, Qian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09596 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques Feng, Pohsun Bi, Ziqian Wen, Yizhu Peng, Benji Liu, Junyu Yin, Caitlyn Heqi Wang, Tianyang Chen, Keyu Zhang, Sen Li, Ming Xu, Jiawei Liu, Ming Pan, Xuanhe Wang, Jinlang Song, Xinyuan Niu, Qian Machine Learning 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. |
| title | Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.09596 |