Samuel Kwabena Owusu

The researcher focuses on understanding and comparing various optimization algorithms in machine learning, particularly Adam, RMSprop, GD, AdaGrad, and AdamW. They systematically compare these methods by evaluating their performance through metrics like mean squared error or validation loss, assessing factors such as convergence speed and generalization ability. Their work aims to optimize training dynamics for machine learning models across diverse datasets and tasks.