Comparative analysis of RMSE and MAP metrics for evaluating CNN and LSTM models.

Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) have made substantial advances in the domains of computer vision and speech recognition in recent years. These deep learning architectures have shown exceptional ability in a variety of tasks. Evaluating the performance of such models is critical for comprehending their efficacy and directing future developments. In this study, we undertake a thorough comparison utilizing two essential evaluation metrics: Root Mean Square Error (RMSE) and Mean Average Precision (MAP). Our research intends to give light on the applicability of these metrics for analyzing the performance of CNNs and LSTMs, as well as their strengths and limitations. [ABSTRACT FROM AUTHOR]