Question 5
A data scientist is training a deep learning model on a massive dataset containing billions of data points. They find that using traditional Batch Gradient Descent (GD) is computationally infeasible. What is the primary reason that makes Batch GD impractical for this task, thus requiring an alternative like Stochastic Gradient Descent (SGD)?
It is impossible to fit the entire large dataset into the computer’s memory(RAM) at once.
The gradient calculated by Batch GD is often too noisy and inaccurate, leadingto poor convergence.
Batch GD is guaranteed to get stuck in sharp local minima, whereas SGD canescape them.
Batch GD requires calculating the gradient of the loss function with respect tothe parameters over the entire dataset for a single update, which is computationally very expensive.