Résumé:
In this chapter, the application of machine learning (ML) in various engineering domains has ushered in transformative advancements, offering solutions to intricate problems and paving the way for unprecedented innovation. Shallow learning methods, known for their simplicity and interpretability, prove effective in scenarios with limited datasets, computational constraints, and straightforward variable relationships. Ideal for engineers seeking practical solutions in challenging data collection environments, these methods excel with smaller datasets. Conversely, deep learning has expanded ML applications by extracting intricate patterns from vast datasets, offering unparalleled accuracy in complex variable relationships. However, this comes at the expense of increased computational requirements, substantial labeled data needs, and reduced interpretability due to higher complexity. In evolving engineering applications, the selection between shallow and deep learning methodologies depends on specific requirements, constraints, and the nature of the problem, requiring practitioners to carefully assess the advantages and disadvantages for optimal utilization of ML in innovative solutions. In this chapter, an exploration of these methodologies has been undertaken across diverse fields of engineering. Additionally, an illustrative example within the realm of electrical engineering has been presented to facilitate a comparative analysis between shallow learning and deep learning methods.
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