Will Deep Learning Change How Teams Execute Big Data Projects?
| Autor: | Jeffrey S. Saltz, Ivan Shamshurin |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Feature engineering
Artificial neural network Computer science business.industry Model selection Deep learning Big data 020207 software engineering 02 engineering and technology Data science 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | IEEE BigData |
| DOI: | 10.1109/bigdata.2018.8622337 |
| Popis: | As data continues to be produced in ever increasing quantities, and technologies such as high performance computing continue to be enhanced, the number of big data projects using advanced neural network machine learning, often referred to as deep learning, continues to increase. Unfortunately, while much has been written on the use of deep learning algorithms in terms of generating insightful analysis, much less has been written about the project management process methodologies that could enable teams to more effectively and efficiently "do" big data deep learning projects. Specifically, the rapid growth in the use of deep learning techniques might introduce new challenges with respect to how to execute a big data deep learning project, due to how deep learning models can learn features automatically. For example, feature engineering and model evaluation phases of big data projects might grow in importance, while other areas, such as model selection, might decrease in importance. Hence, this paper discusses the key research questions relating the potential impact of the use of deep learning on how teams should execute big data projects. |
| Databáze: | OpenAIRE |
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