random sampling batching
[ˈrændəm ˈsæmplɪŋ ˈbætʃɪŋ]
nounpl: random sampling batchings
amostragem aleatória em lotes
1. A statistical and computational technique that divides data into random batches or groups for sampling, analysis, or processing purposes, commonly used in machine learning, quality control, and data analysis
The factory implemented random sampling batching to inspect product quality more efficiently across production runs.
A fábrica implementou amostragem aleatória em lotes para inspecionar a qualidade do produto de forma mais eficiente entre as corridas de produção.
2. In machine learning, a method of dividing training data into random batches to improve model training efficiency and generalization
Random sampling batching helps prevent overfitting by exposing the neural network to diverse data subsets during training.
A amostragem aleatória em lotes ajuda a evitar o sobreajuste, expondo a rede neural a diferentes subconjuntos de dados durante o treinamento.
This is technical terminology predominantly used in academic, industrial, and data science contexts in both Brazil and the United States. It is essential vocabulary in quality control departments, machine learning research, and data analysis teams. The term is increasingly important in Brazilian tech companies and manufacturing sectors as these industries modernize their quality assurance and AI implementation processes.
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