Inspired by the success of deep learning in computer vision tasks, deep learning approaches for various MRI problems have been extensively studied in recent years. Early deep learning studies for MRI reconstruction and enhancement were mostly based on image-domain learning. However, because the MR signal is acquired in the k-space domain, researchers have demonstrated that deep neural networks can be directly designed in k-space to utilize the physics of MR acquisition. In this chapter, the recent trend of k-space deep learning for MRI reconstruction and artifact removal are reviewed. First, scan-specific k-space learning, which is inspired by parallel MRI, is covered. Then we provide an overview of data-driven k-space learning. Subsequently, unsupervised learning for MRI reconstruction and motion artifact removal are discussed.