arXiv:2410.09596
Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
From Tiny Machine Learning to Tiny Deep Learning: A Survey
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data Imbalance
Auto-nnU-Net: Towards Automated Medical Image Segmentation
SEAL: Searching Expandable Architectures for Incremental Learning
AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.