 Sweeping through the parameter space for optimal parameters may not be feasible due to the cost in time and computational resources.Various tricks, such as batching (computing the gradient on several training examples at once rather than individual examples) speed up computation.Large processing capabilities of many-core architectures (such as GPUs or the Intel Xeon Phi) have produced significant speedups in training, because of the suitability of such processing architectures for the matrix and vector computations.