EXPLAINABLE AI: APPROACHES TO MAKE MACHINE LEARNING MODELS MORE TRANSPARENT AND UNDERSTANDABLE FOR HUMANS.

Abstract

The abstract provides a concise summary of the entire paper, highlighting the objectives, methodology, key findings, and conclusions. It serves as a snapshot of the research study and its contributions. Transfer learning is a powerful approach that leverages knowledge from one domain to enhance learning in another domain. This paper explores various methods and techniques employed in transfer learning and investigates their effectiveness in improving learning outcomes.  The abstract is a concise summary of the entire paper. It provides a brief overview of the research objectives, methodology, key findings, and conclusions. The abstract is typically limited to a specific word count and serves as a snapshot of the paper, allowing readers to quickly grasp the main contributions and significance of the study. The study employs a combination of theoretical analysis and experimental evaluation to assess the benefits and limitations of transfer learning across different domains. The results demonstrate the potential of transfer learning to enhance learning performance and highlight key factors influencing its success. The study also identifies factors such as domain similarity, labeled data availability, and choice of transfer learning method as crucial determinants of transfer learning success. The implications of these findings suggest that transfer learning can address the challenge of limited labeled data in the target domain and accelerate the learning process. The research contributes to the understanding of transfer learning and provides practical insights for researchers and practitioners interested in applying transfer learning techniques