Enhancement Computer Vision using Deep Learning Optimization Techniques

Deep learning has witnessed remarkable advancements in recent years, revolutionizing fields such as computer vision and reinforcement learning. A critical aspect of training deep neural networks is the choice of optimization techniques, which directly impacts convergence speed, model performance, and computational efficiency. This research paper provides a comprehensive survey and comparative analysis of various optimization techniques employed in deep learning models. The fundamental optimization algorithms commonly used in deep learning, including stochastic gradient descent (SGD), momentum-based methods, adaptive learning rate methods (RMSprop, AdaGrad), and newer variants like Adam, AdaDelta, and Nadam. Divining into the underlying principles and mechanisms of each optimization algorithm, elucidating their strengths and limitations. This study’s experimental analysis compares the performance of several optimizers in terms of validation accuracy, validation loss, and computation time over different epochs. According to the results, the Adam optimizer consistently gives the highest accuracy within an acceptable computation time. It provides guidance for selecting the most suitable optimization method for specific deep learning tasks and sheds light on emerging trends that hold promise for further improving the efficiency and effectiveness of deep learning model training.