The eighty percent of the matter in the Universe is in the form of dark matter that comprises the skeleton of the large-scale structure called the Cosmic Web. As the Cosmic Web dictates the motion of all matters in galaxies and intergalactic media through gravity, knowing the distribution of dark matter is essential for studying the large-scale structure. However, as dominated by dark matter and warm-hot inter-galactic media, both of which are hard to trace, the detailed structure of the Cosmic Web is unknown. Here we show that we can reconstruct the Cosmic Web from the galaxy distribution using the convolutional-neural-network based deep-learning algorithm. We find the mapping between the position and velocity of galaxies and the Cosmic Web using the results of the state-of-the-art cosmological galaxy simulations, Illustris-TNG. We confirm the mapping by applying it to the EAGLE simulation. Finally, using the local galaxy sample from Cosmicflows-3, we find the dark-matter map in the local Universe. We anticipate that the local dark-matter map will illuminate the studies of the nature of dark matter and the formation and evolution of the Local Group. High-resolution simulations and precise distance measurements to local galaxies will improve the accuracy of the dark-matter map.