training

Training is the process of fitting a model’s parameters to data by minimizing or optimizing a carefully chosen objective (loss or surrogate) via gradient-based (or other) optimization, typically using forward and backward passes.

In practice, training workflows include:

• Specifying the objective (loss) and evaluation metrics
• Splitting the data into training, validation, and test sets
• Iterating over mini-batches across multiple epochs or streaming passes
• Updating weights with optimizers
• Applying regularization techniques, such as L1/L2 weight decay, dropout, normalization, data augmentation, early stopping, checkpointing, gradient clipping, and learning rate scheduling
• Optionally using a pretraining or fine-tuning pipeline
• Enhancing efficiency and stability through batching, normalization, mixed precision arithmetic, gradient accumulation, distributed / parallel training, and use of accelerated hardware (GPUs, TPUs)

Modern training often operates in overparameterized regimes, where implicit biases of optimization dynamics and early regularization can influence generalization.