What is the difference between SGD and TGD?

If you choose stochastic gradient descent (SGD) in the context of conflict between stochastic gradient descent vs gradient descent then it would help you in dealing with the large datasets. In the circumstances where the sizes of the data make batch processing impractical, the SGD is very useful in this particular scenario. The gradient descent is known for computing the gradient of the entire data set even before the updation of the parameters while SGD is famous for processing a random subset for each iteration.

Therefore, the adaptability feature of SGD makes it more comfortable, and efficient and facilitates in easy model training. Moreover, the stochastic nature provides you with randomness which can further assist you in escaping through local minima which can further lead to better convergence. 

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