Recent Advances in Stochastic Gradient Descent in Deep Learning

In the age of artificial intelligence, the best approach to handling huge amounts of data is a tremendously motivating and hard problem. Among machine learning models, stochastic gradient descent (SGD) is not only simple but also very effective. This study provides a detailed analysis of contemporary state-of-the-art deep learning applications, such as natural language processing (NLP), visual data processing, and voice and audio processing. Following that, this study introduces several versions of SGD and its variant, which are already in the PyTorch optimizer, including SGD, Adagrad, adadelta, RMSprop, Adam, AdamW, and so on. Finally, we propose theoretical conditions under which these methods are applicable and discover that there is still a gap between theoretical conditions under which the algorithms converge and practical applications, and how to bridge this gap is a question for the future.

Automated summary

In the age of artificial intelligence, finding the best approach to handle massive data is a challenging task. Stochastic gradient descent (SGD) stands out among machine learning models as it is simple yet highly effective. This study examines various contemporary deep learning applications, including natural language processing (NLP), visual data processing, and voice and audio processing. The study also presents different versions of SGD and its variant available in the PyTorch optimizer, such as SGD, Adagrad, adadelta, RMSprop, Adam, AdamW, etc. Additionally, theoretical conditions for the applicability of these methods are proposed, highlighting the existing gap between theoretical convergence and practical implementation.