In apprentissage automatique, a mini-batch is a small, randomly selected subset of the full training dataset that is used to update the model’s parameters during training. This approach allows for a balance between the efficiency of batch processing and the robustness of stochastic algorithme de descente de gradient (SGD). Instead of using the entire dataset for each mise à jour des paramètres, which can be computationally expensive and time-consuming, mini-batch training processes a fraction of the data at a time.
Mini-batches typically range in size from a few examples to several hundred, depending on the dataset and the model being trained. The choice of mini-batch size can significantly affect the training dynamics, convergence speed, and final performance du modèle. A smaller mini-batch may lead to noisier updates but can provide a more exploratory training process, while a larger mini-batch may stabilize the convergence but at the cost of potentially missing the optimal solution.
L'utilisation de mini-lots aide également à exploiter le le traitement parallèle capabilities of modern hardware, such as GPUs, making the training process faster and more efficient. Additionally, mini-batch training facilitates online learning scenarios where data arrives in streams, allowing the model to update frequently without needing the entire dataset at once.
Overall, mini-batch training is a widely used technique in deep learning and other areas of machine learning, balancing l'efficacité computationnelle avec un apprentissage efficace.