Model Training

What Is Model Training?

Model training is where AI learns. The process involves presenting examples to the model, measuring how wrong its predictions are, and adjusting internal parameters (weights) to reduce errors. This iterative cycle repeats over thousands or millions of examples across multiple epochs (complete passes through the data) until the model converges on a good solution.

Training involves several critical decisions: what data to use, how long to train, what learning rate to use, what batch size to apply, and when to stop to avoid overfitting. Overfitting occurs when a model memorizes training data rather than learning generalizable patterns, performing poorly on new data. Techniques like regularization, dropout, and validation monitoring help prevent overfitting.

Training is computationally expensive, especially for large models. Training GPT-3 cost millions of dollars and required specialized hardware. For most applications, transfer learning is more practical: start with a pre-trained model and fine-tune it on your specific data, which is much cheaper and faster than training from scratch.