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 and provides practical examples using DNALLM for DNA language models.

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 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