Training

Training is the process of teaching a machine learning model to recognize patterns and make accurate predictions by adjusting its internal parameters based on large amounts of data. It's the foundational phase where models learn from examples. During training, a model is presented with input data and corresponding correct outputs (labels). The model makes predictions, compares them to the correct answers, and calculates how wrong it was (the loss). Using optimization algorithms like stochastic gradient descent, the model adjusts its parameters to reduce this loss. This process repeats over many iterations until the model's predictions become accurate. Training requires substantial computational resources, particularly for large models. Modern language models are trained on hundreds of billions of tokens of text using specialized hardware like GPUs and TPUs. Training can take weeks or months and cost millions of dollars for the largest models. The quality of training depends on data quality, quantity, diversity, and how well the training process is managed. Different training approaches serve different purposes. Supervised learning uses labeled data to teach models specific tasks. Unsupervised learning finds patterns in unlabeled data. Reinforcement learning teaches models through rewards and penalties. Transfer learning leverages knowledge from one task to improve performance on another. Fine-tuning adapts pre-trained models to specific domains or tasks. Training is distinct from inference: training is expensive and happens once or periodically, while inference is cheaper and happens continuously. Understanding training is important for appreciating why large models are expensive to create, why data quality matters, and why fine-tuning is often more practical than training from scratch.