Pre-training

Pre-training is a fundamental technique in machine learning, especially prevalent in deep learning, where a model is initially trained on a very large, diverse dataset before being fine-tuned for a more specific task. During this initial phase, the model learns general features, representations, and underlying patterns inherent in the vast dataset, often through self-supervised learning objectives (e.g., predicting missing words in a sentence or reconstructing masked parts of an image). This process allows the model to develop a robust understanding of the data's structure and semantics without explicit human labels.