Embodiments herein achieve a machine-learning system for managing shuffling of input training datasets. The machine-learning system includes a training dataset manager configured to shuffle an input dataset received from each of a plurality of electronic devices. Further, the training dataset manager is configured to split the input training datasets into a plurality of mini-batches. Each of the mini-batches, along with the target values, defines an error surface corresponding to an error function. A learning manager is configured to obtain a cross mini-batch discriminator based on the error function for each of the mini-batches. Further, the learning manager is configured to select a mini-batch associated with a least cross mini-batch discriminator from the plurality of mini-batches as optimal mini-batch.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine-learning system for managing shuffling of input training datasets, the machine-learning system comprising: a training dataset manager configured to: shuffle an input dataset received from each of a plurality of electronic devices; and split the input training datasets into a plurality of mini-batches, wherein each of the mini-batches define an error surface corresponding to an error function; and a learning manager configured to: obtain a cross mini-batch discriminator based on the error function for each of the mini-batches; and select a mini-batch associated with a least cross mini-batch discriminator from the plurality of mini-batches as optimal mini-batch. 2. The machine-learning system of claim 1, wherein the cross mini-batch discriminator is estimated as a function of a difference in an error converged on at least one specific mini-batch configuration from the plurality of mini-batch configurations using a gradient descent based method, wherein the error on the error surface is formed by a subsequent mini-batch from the plurality of mini-batches. 3. The machine-learning system of claim 2, wherein the gradient descent based method is performed as an attempt to reach a minimum point on the error surface defined by each of the mini-batches. 4. The machine-learning system of claim 2, wherein the least cross mini-batch discriminator is equivalent for minimizing the difference in the error between error surfaces of the at least one specific mini-batch and the subsequent mini-batch leads to faster convergence. 5. The machine-learning system of claim 1, wherein the error surface is characterized based on training input and target values corresponding to each of the mini-batches. 6. The machine-learning system of claim 1, wherein the error surface formed from the mini-batches is the Mean Squared Error surface. 7. The machine-learning system of claim 1, wherein the error surface is defined as an arbitrary function with at least one minima, defined over the mini-batches. 8. The machine-learning system of claim 1, wherein the error surface is dynamically changed while traversing from one mini-batch to other mini-batch in the plurality of mini-batches. 9. The machine-learning system of claim 1, wherein the cross mini-batch discriminator is minimized using a brute force method. 10. The machine-learning system of claim 9, wherein the brute force method is used to evaluate the cross mini-batch discriminator for a plurality of a set of mini-batches which are formed by random permutations of the input datasets. 11. The machine-learning system of claim 1, wherein the cross mini-batch discriminator is minimized by posing the cross mini-batch discriminator minimization as one of a non-linear optimization problem, a sub modular optimization problem, a probabilistic formulation, and an optimization formulation. 12. A method to manage shuffling of input training datasets to train a machine-learning system, the method comprising: shuffling, by a training dataset manager, an input dataset received from each of a plurality of electronic devices; splitting, by the training dataset manager, the input training datasets into a plurality of mini-batches, wherein each of the mini-batches define an error surface corresponding to an error function; obtaining, by a learning manager, a cross mini-batch discriminator based on the error function for each of the mini-batches; and selecting a mini-batch associated with a least cross mini-batch discriminator from the plurality of mini-batches as optimal mini-batch. 13. The method of claim 12, wherein the cross mini-batch discriminator is estimated as a function of a difference in an error converged on at least one specific mini-batch from the plurality of mini-batches using a gradient descent based method, wherein the error on the error surface is formed by a subsequent mini-batch from the plurality of mini-batches. 14. The method of claim 13, wherein the gradient descent based method is performed as an attempt to reach a minimum point on the error surface defined by each of the mini-batches. 15. The method of claim 13, wherein the least cross mini-batch discriminator is equivalent to minimizing the difference in the error between error surfaces of the at least one specific mini-batch and the subsequent mini-batch leads to faster convergence. 16. The method of claim 12, wherein the error surface is characterized based on training input and target values corresponding to each of the mini-batches. 17. The method of claim 12, wherein the error surface formed from the mini-batches is the Mean Squared Error surface. 18. The method of claim 12, wherein the error surface is defined as an arbitrary function with at least one minima defined over the mini-batches. 19. The method of claim 12, wherein the error surface is dynamically changed while traversing from one mini-batch to other mini-batch in the plurality of mini-batches. 20. The method of claim 12, wherein the cross mini-batch discriminator is minimized using a brute force method. 21. The method in claim 20, wherein the brute force method is used to evaluate the cross mini-batch discriminator for a plurality of a set of mini-batches which are formed by random permutations of the input datasets. 22. The method of claim 12, wherein the cross mini-batch discriminator is minimized by posing the cross mini-batch discriminator minimization as one of a non-linear optimization problem, a sub modular optimization problem, a probabilistic formulation, and an optimization formulation.