One big advantage of SGD is that it is much faster than regular gradient descent. Instead of looking at all the data at once, it updates the model using only small parts of the data at a time. This makes it quicker and allows it to work with very large datasets that would be too slow for traditional methods.

SGD also helps the model avoid getting stuck in bad solutions. Since it takes small and random steps, it can escape local minimum points and find better solutions in some cases. This makes it useful for complex problems where there are many possible answers.

Another benefit is that it uses less memory because it does not need to store the entire dataset at once. This makes it possible to train models even on computers with limited resources.

SGD is also flexible. It can be combined with other techniques like momentum and learning rate adjustments to improve accuracy and stability. This helps in training deep learning models efficiently.

Because of these advantages, SGD is one of the most widely used optimization methods in machine learning and deep learning.