Defining which strategies to avoid overfitting in deep learning are best objectively is difficult. Numerous technological and human factors and contexts are involved in big data (Matthews, 2018). The effectiveness of such techniques as early stopping, regularization, entropy weighting, data augmentation, and additional training data depends on such subjective variables as neural network’ structure, data sets’ contents, and the expertise and professional skills of the responsible data engineer. However, the first two models can be considered comparatively better than the others listed since they are quick to execute. These measures are relatively simple in terms of intervention in the fitting procedure and the implementation of new commands and adjustments (Baheti, 2022). They generate no data noise and do not complicate the structure of the model; early stopping and regularization also have the property of universality (Baheti, 2022). These technical qualities are central and crucial in such a time-consuming industry as neural networks.
Techniques for improving and correcting neural networks analyzed are not ideal, and each of them has its limitations and disadvantages. Many of them are generated by the need to maintain a balanced state between bias and variance (Belkin et al., 2019). For example, regularization causes an engineer’s date to make their model less representative of its data set (Wickramasinghe, 2021). Conversely, early stopping creates a high bias in the neural network (Wüthrich, 2020). Data augmentation is a relatively safe method of adjustment, but it is knowledge- and human resource-intensive (Soni, 2022). Adding additional training data runs the risk of overcomplicating the fitting process and requires significant precision and accuracy from the observer (Baheti, 2022). Entropy weighting is a relatively new way to prevent overfitting, and little knowledge about its adverse effects exists, which is its primary limitation (Kumar et al., 2021). The topic of the superiority of some anti-overfitting interventions over others is highly subjective and contextual.
Digitalization and high technology have brought a data-driven approach to organizational operations and management. Big data merged with analytics and partially replaced them in the business world, which led to the intensification of its productivity (McAfee and Brynjolfsson, 2012). Expectedly, companies closely related to software and marketing fields have proven to be the primary beneficiaries of the emergence and implementation of practical big data methodologies (Provost and Fawcett, 2013). One of these is Neptune, whose focus is on developing and improving processes associated with artificial data mining and the interpretation of various information (Sanghvi, 2022). They extensively use early stopping, advise others of this practice, and provide related guidance. V7 is another data, computer, and programming specialist team that directly deals with deep learning (Baheti, 2022). They apply all currently known anti-overfitting techniques, including regularization. They find this neural network correction method versatile and easy to use.
Reference List
Baheti, P. (2022) What is overfitting in deep learning and how to avoid it. Web.
Belkin, M. et al. (2019) ‘Reconciling modern machine learning practice and the bias-variance trade-off’, PNAS, 116 (32). Web.
Kumar, R. et al. (2021) ‘Revealing the benefits of entropy weights method for multi-objective optimization in machining operations: A critical review’, Journal of Materials Research and Technology, 10, pp. 1471-1492.
Matthews, K. (2018) Understanding subjectivity in data science. Web.
McAfee, A. and Brynjolfsson, E. (2012) ‘Big data: The management revolution’, Harvard Business Review, Web.
Provost, F. and Fawcett, T. (2013) Data science for business: What you need to know about data mining and data-analytic thinking. Sebastopol, CA: O’Reilly Media.
Sanghvi, R. (2022) Early stopping with Neptune. Web.
Soni, P. (2022) Data augmentation: Techniques, benefits and applications. Web.
Wickramasinghe, S. (2021) Bias & variance in machine learning: Concepts & tutorials. Web.
Wüthrich, M. V. (2020) ‘Bias regularization in neural network models for general insurance pricing’, European Actuarial Journal, 10(1), pp. 179-202.