Training refers to the iterative process of adjusting a model’s parameters to converge on weights that optimally replicate the training data. This process involves feeding input data into the model, computing predictions, calculating the difference between predictions and actual outputs (loss), and using optimization algorithms to iteratively update the model’s parameters until the loss is minimized.
