Description: The training loop is a fundamental process in machine learning that involves the continuous iteration over a dataset to adjust a model’s parameters. This process is carried out over multiple epochs, where each epoch represents a complete pass through the training dataset. During each iteration, the model makes predictions, calculates the error or loss based on the predictions and the actual labels, and then adjusts its parameters using optimization algorithms like gradient descent. This cycle is repeated until the model reaches an acceptable level of accuracy or a stopping criterion is met. The training loop is crucial because it allows the model to learn patterns and relationships in the data, improving its ability to generalize to new unseen data. The structure of the training loop includes model initialization, loss function definition, optimizer selection, and the execution of the training cycle, where model parameter updates are performed. This iterative process is essential for developing effective and robust machine learning models.
