Analysis of AutoML Tools in the World of Automated Deep Learning
DOI:
https://doi.org/10.63995/FXPC8243Keywords:
AutoML; Automated deep learning; Data preprocessing; Hyperparameter tuning; Machine learning pipeline; Model selection; Performance evaluationAbstract
In-depth analysis of AutoML (Automated Machine Learning) tools in the context of automated deep learning, a rapidly evolving field aimed at simplifying and optimizing the development of deep learning models. AutoML tools automate various stages of the machine learning pipeline, including data preprocessing, feature selection, model selection, hyperparameter tuning, and model evaluation, making them accessible to non-experts and enhancing productivity for experts. The article reviews prominent AutoML tools, examining their methodologies, strengths, limitations, and performance across different tasks. Through comparative studies and practical applications, the effectiveness of these tools in producing robust and efficient deep learning models is evaluated. Additionally, the article explores emerging trends and future directions in the integration of AutoML with deep learning, highlighting the potential for these tools to revolutionize machine learning workflows and democratize access to advanced AI technologies.
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