Iteración
Una iteración es un ciclo de cálculo en el entrenamiento de un modelo de IA, que consiste en pasar un lote de datos (batch) a través del modelo. En cada iteración se calcula la pérdida y se actualizan los parámetros del modelo. Varias iteraciones componen una época (epoch), es decir, un recorrido completo del conjunto de entrenamiento.
Contexto
La noción de iteración está directamente vinculada al uso de mini-batches, una estrategia que equilibra eficiencia y precisión. Esto permite entrenar modelos complejos en grandes volúmenes de datos sin requerir memoria excesiva.
Ejemplos
- En un modelo de visión artificial, una iteración implica procesar un lote de imágenes, calcular la función de pérdida y ajustar los pesos mediante retropropagación.
- En un sistema de traducción automática, cada iteración equivale a procesar frases en paralelo dentro de un lote.
Ventajas y limitaciones
- ✅ Escalable a grandes conjuntos de datos.
- ✅ Facilita el seguimiento del aprendizaje en detalle.
- ❌ Tamaños de batch inadecuados pueden provocar ruido o lentitud.
Iterations are the building blocks of the optimization process in machine learning. By repeatedly adjusting model parameters through many cycles, the algorithm progressively minimizes the loss function. The quality of each update depends not only on the batch size but also on the learning rate. A high learning rate may cause the model to diverge, while a very small one can slow convergence, requiring thousands of additional iterations.
Iterations are often grouped into epochs, which represent a full pass over the training dataset. However, convergence does not always happen neatly at epoch boundaries: sometimes significant improvements occur within early iterations, while later ones provide only fine-tuning. Monitoring performance metrics across iterations—such as training loss, validation accuracy, or gradient norms—helps practitioners detect underfitting, overfitting, or vanishing gradients.
In deep learning frameworks like TensorFlow or PyTorch, iterations are executed automatically within training loops, but they can be customized with callbacks or schedulers. For example, learning rate schedules adjust the step size dynamically at specific iterations, and checkpointing allows saving the model at intervals to resume training later. This flexibility ensures that iterations are not just repetitive cycles but also opportunities for adaptive strategies.
From a practical standpoint, the efficiency of iterations is deeply linked to hardware accelerators such as GPUs or TPUs. Optimized batch processing, parallelism, and memory management directly impact how many iterations can be executed per second. As a result, iteration design is not only a mathematical concept but also a key engineering concern for scaling machine learning to industrial workloads.
📚 Referencias
- Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow.
