Training Model

Training models encompasses the process of optimizing machine learning algorithms to achieve high performance on specific tasks. Current research emphasizes improving model efficiency and robustness, focusing on techniques like curriculum learning (gradually increasing training difficulty), parameter-efficient fine-tuning (adapting pre-trained models with minimal changes), and addressing data imbalances through methods such as data augmentation and novel loss functions. These advancements are crucial for deploying models in resource-constrained environments and for tackling real-world challenges in diverse fields, including healthcare, natural language processing, and computer vision, where data scarcity and heterogeneity are common.