Description: Sample efficiency in neural networks refers to a model’s ability to achieve good performance with a limited amount of training data. This concept is crucial in the field of machine learning, where data availability can be a limiting factor. An efficient model in terms of samples can generalize well from a small set of examples, meaning it can make accurate predictions on unseen data without requiring a large volume of information. This feature is especially valuable in situations where data collection is costly or challenging, such as in medical applications or specialized data analysis. Sample efficiency relates to the complexity of the model and its ability to learn meaningful patterns without overfitting to the training data. In summary, a model that exhibits high sample efficiency can maximize its performance from a limited data set, making it more practical and applicable in various real-world situations.
