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Expert Systems With Applications 230 (2023) 120534Contents lists available at ScienceDirectExpert Systems With Applications journal homepage: www.elsevier.com/locate/eswaAn efficient deep learning
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What is an efficient deep learning?
Efficient deep learning refers to techniques and practices aimed at optimizing deep learning models to improve their performance while reducing computational resource requirements, training time, and energy consumption.
Who is required to file an efficient deep learning?
Individuals and organizations that conduct deep learning research or deploy deep learning models, particularly in regulated industries or for reporting purposes, may be required to file an efficient deep learning, typically to ensure compliance with industry standards.
How to fill out an efficient deep learning?
Filling out an efficient deep learning involves documenting the model architecture, training processes, evaluation metrics, and resource usage. It is essential to provide clear descriptions of methodology, data sources, and any optimization techniques used.
What is the purpose of an efficient deep learning?
The purpose of an efficient deep learning is to facilitate the effective application of deep learning technologies by minimizing resource usage, ensuring compliance, and enhancing generalizability and performance of models in real-world applications.
What information must be reported on an efficient deep learning?
Information that must be reported includes model architecture, data preprocessing methods, training and validation datasets, hyperparameters, evaluation metrics, and any performance benchmarks relevant to the context.
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