This paper proposes a training set synthesis technique for data-efficient learning, called Dataset Condensation, that learns to condense a large dataset into a small set of informative samples for training deep neural networks from scratch. We formulate this goal as a gradient matching problem between the gradients of a deep neural network trained on the original data and our synthetic data.