Exploring the Impact of Transfer Learning on AI Model Performance in Data Science

Krish Salecha Daga

Authors

Krish Salecha Daga Author

Keywords:

Transfer learning, AI models, Data Science, natural language processing

Abstract

Transfer learning is a technique in deep learning that leverages pre-trained models to improve the performance of new models on similar tasks. This approach has revolutionized the field of data science by significantly reducing the need for large amounts of labeled data and computational resources. This study investigates the impact of transfer learning on the performance of AI models in various data science applications. We examine the effectiveness of transfer learning in different scenarios, including image classification, natural language processing, and time series forecasting. Our results show that transfer learning can lead to substantial improvements in model accuracy and efficiency, particularly when working with limited data. We also discuss the limitations and potential pitfalls of transfer learning and provide recommendations for its effective implementation. This research aims to provide data scientists with a comprehensive understanding of the benefits and challenges of transfer learning, enabling them to make informed decisions about its application in their projects.

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