Does Minibatch reduce drawback of SGD?
For gradient descent, batch updates calculate the weights based on all examples. This process continues until convergence which is typically measured by a derivative of zero. The resulting weights might end up in a local minimum or saddle point because both have a derivative of zero. 

Stochastic gradient descent takes a random sampling of examples, introducing randomness into the updates. This randomness increases the possibilities of escaping a local minimum or saddle point. The updates might randomly overestimate the area with a derivative of zero and find a new section of the error surface that leads to a better optimum.