What is NOT the advantage of using Stochastic Gradient Descent $($SGD$)$ over other gradient descent methods, such as full$-$batch, in online learning?

Group of answer choices

SGD can update the model parameters with each new data point, allowing for continuous learning.

SGD converges faster.

SGD requires access to the entire dataset within each iteration.

SGD is less computationally expensive.