Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques
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.
Code (0)
등록된 구현이 없습니다.
Tasks
AutoMLNeural Architecture SearchSimilar Papers 제목 키워드 기반
AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text
The process of extracting knowledge from natural language text poses a complex problem that requires both a combination of machine learning techniques and proper feature selection. Recent advances in Automatic Machine Le…
AutoMLBIG-bench Machine Learningfeature selectionEvolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution
The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of featu…
AutoMLBIG-bench Machine LearningGeneral ClassificationEvolutionary Neural AutoML for Deep Learning
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters. Such a …
AutoMLDeep LearningDistributed ComputingEvolutionary Algorithms+5Evolving Executable Pipeline Programs for AutoML with Language Models
Automated machine learning (AutoML) systems search for pipelines within a space of preprocessing operators, learners, and hyper-parameters specified in advance: they can select and tune known components, but cannot produ…
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models
Automated Machine Learning (AutoML) has simplified complex ML processes such as data pre-processing, model selection, and hyper-parameter searching. However, traditional AutoML frameworks focus solely on discriminative t…
AutoMLModel Selection