Long efforts have been made to enable machines to understand human language. Nowadays such activities fall under the broad umbrella of machine comprehension. The results are optimistic due to the recent advancements in the field of machine learning. Deep learning promises to bring even better results but requires expensive and resource hungry hardware. In this paper, we demonstrate the use of deep learning in the context of machine comprehension by using non-GPU machines. Our results suggest that the good algorithm insight and detailed understanding of the dataset can help in getting meaningful results through deep learning even on non-GPU machines. © 2019, Dr D. Pylarinos. All rights reserved.