Title: Learning to Predict Parameter for Unseen Data. Abstract: Typical deep learning models depend heavily on large amounts of training data and resort to an iterative optimization algorithm (e.g., SGD or Adam) for learning network parameters, which makes the training process very time- and resource-intensive. In this paper, we propose a new training paradigm and formulate network parameter training into a prediction task:  given a network architecture, we observe there exists correlations between datasets and their corresponding optimal network parameters, and explore if we can learn a hyper-mapping between them to capture the relations, such that we can directly predict the parameters of the network for a new dataset never seen during the training phase. To do this, we put forward a new hypernetwork with the purpose of building a mapping between datasets and their corresponding network parameters, and then predict parameters for unseen data with only a single forward propagation of the hypernetwork. At its heart, our model benefits from a series of GRU sharing weights to capture the dependencies of parameters among different layers in the network. Extensive experimental studies are performed and experimental results validate our proposed method achieves surprisingly good efficacy. For instance, it takes 119 GPU seconds to train ResNet-18 using Adam from scratch and obtain a top-1 accuracy of 74.56%, while our method costs only 0.5 GPU seconds to predict the network parameters of ResNet-18 achieving comparable performance (73.33%), more than 200 times faster than the traditional training paradigm.