Optimal Gradient Descent Algorithm for LSTM Neural Network Learning

This research paper delves into an in-depth comparison of different gradient descent methods tailored for optimising long short-term memory (LSTM) recurrent neural networks (RNNs) using data generated from sinusoids with varying noise levels. Gradient descent is an optimisation technique extensively employed in machine learning, deep learning, and diverse scientific domains and serves as the focal point of investigation. This study's primary objective is to thoroughly comprehend the distinctive attributes, merits, and limitations of various gradient descent methods, thereby elucidating their effects on convergence speed, stability, and computational efficiency within the context of LSTM RNNs. Through meticulous analysis and experimentation, this research contributes insights essential for informed decision-making in selecting and deploying gradient descent algorithms for LSTM RNN optimisation, thereby advancing the efficacy and reliability of recurrent neural network applications in real-world scenarios.