DESIGN AND IMPLEMENTATION OF PROJECT MANAGEMENT SYSTEMS USING PRIORITY SCHEDULING (CASE STUDY FEDERAL POLYTECHNIC DAMATURU CONSTRUCTION AND FURNISHINGS OF SKILLS DEVELOPMENT CENTRE)

Transfer learning has emerged as a powerful technique in the field of machine learning and artificial intelligence, enabling the reuse of knowledge gained from one task to improve performance on a different but related task. Unlike traditional learning approaches that require large amounts of labeled data, transfer learning leverages pre-trained models to achieve competitive results with significantly less data and reduced computational resources. This approach is particularly effective in domains where data collection is expensive, time-consuming, or limited, such as medical imaging, natural language processing, and speech recognition. By adapting and fine-tuning pre-trained models, transfer learning not only accelerates the training process but also enhances accuracy, generalization, and robustness in new tasks. This seminar explores the principles of transfer learning, its methodologies, practical applications, and the challenges associated with domain adaptation and negative transfer. The study highlights the growing importance of transfer learning as a bridge between theoretical research and real-world applications, making it a crucial strategy for advancing modern intelligent systems.