Problem statementSend feedbackDo proper match for below statements:1. Uses n data points instead of 1 sample at each iteration.2. Computes the gradient using a single sample.3. Computes the gradient using the whole dataset.Types of Gradient DescentA: Mini-batch gradient descent B: Stochastic gradient descent C: Batch gradient descent

Under which situations will you prefer to go for "Stochastic gradient descent" and give recommendations accordingly?Select an option Clear ResponseCalculations are done in such a way that the training data instance and its updates are calculated immediately.Calculations are done in such a way that the training data instance and its updates are calculated as a batch process.Calculations are done in such a way that the training data instance and its updates are calculated every day at a particular time.Calculations are done in such a way that the training data instance is calculated immediately, but its updates will happen in a batch.

What is correct about stochastic gradient descent? (select all that apply)1 pointThe loss may exhibit sudden increases The loss must be linearIt's an approximation of batch gradient descent

1. Mention the advantages of Stochastic gradient descent.

Select all variations of gradient descent that are discussed in the video

The ______________ optimization algorithm updates weights more frequently than batch gradient descent by using one training example at a time.