Coded computation is an emerging technique which uses error correction codes to improve the reliability of distributed computation systems. Roughly speaking, a big computational task is divided into smaller subtasks along with additional redundancies. Coded computation schemes guarantee that the overall computational tasks can be accomplished even if some of the smaller subtasks are not completed. Coded computation for simple calculation tasks, e, g, matrix multiplications are relatively understood. However, coded computation for general functions is a very open question. In this project, we study the coded computation of both linear and general functions with different methods (including deep neural networks). We also investigate its potential applications for solving large-scale machine learning problems.