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Training Concepts in Machine Learning

Training is the fundamental process in machine learning where a model learns patterns from data to make predictions or perform tasks. This document explains the core concepts of training as they apply to DNA large language models. For hands-on DNALLM fine-tuning walkthroughs, see the Fine-tuning guide (user_guide/fine_tuning/) and the worked case studies (user_guide/case_studies/).

What is Training in Machine Learning?

Training is the process of optimizing a model's parameters (weights and biases) to minimize a loss function, enabling the model to learn meaningful patterns from data. In the context of DNA large language models, training involves:

  • Parameter Optimization: Adjusting model weights to minimize prediction errors
  • Pattern Learning: Discovering biological patterns and relationships in DNA sequences
  • Representation Learning: Learning meaningful embeddings for DNA tokens and sequences
  • Task Adaptation: Fine-tuning for specific biological tasks and applications