Pre training

Pre training is the initial phase of machine learning model development where neural networks learn general patterns, features, and representations from large, unlabeled datasets before being fine-tuned for specific tasks. This foundational training process enables models to acquire broad knowledge about data distributions, linguistic patterns, or visual features that can be transferred to downstream applications. Pre training typically involves self-supervised learning objectives such as masked language modeling, next token prediction, or contrastive learning that allow models to learn from raw data without human annotations. This approach is fundamental to transformer models like GPT and BERT, where extensive pre training on diverse text corpora creates versatile language representations. Pre training reduces the computational requirements and data needs for subsequent task-specific training while improving model performance across various applications. The pre training phase establishes the foundational knowledge base that enables transfer learning, few-shot learning, and adaptation to specialized domains with minimal additional training.