This paper provides an overview of deep learning in medical imaging, with a particular focus on MRI. It highlights the recent advancements and challenges in applying machine learning to medical image processing and analysis. The authors discuss the broad scope of deep learning, its applications in various fields, and its potential impact on healthcare. The paper also delves into the technical aspects of deep learning, including artificial neural networks and convolutional neural networks (CNNs), and their specific applications in MRI, such as image acquisition, signal processing, image reconstruction, quantitative parameter estimation, image restoration, super-resolution, and image synthesis. The authors aim to provide a comprehensive yet concise introduction to the field, offering pointers to core references, educational resources, and open-source code for those interested in contributing to this rapidly evolving area.This paper provides an overview of deep learning in medical imaging, with a particular focus on MRI. It highlights the recent advancements and challenges in applying machine learning to medical image processing and analysis. The authors discuss the broad scope of deep learning, its applications in various fields, and its potential impact on healthcare. The paper also delves into the technical aspects of deep learning, including artificial neural networks and convolutional neural networks (CNNs), and their specific applications in MRI, such as image acquisition, signal processing, image reconstruction, quantitative parameter estimation, image restoration, super-resolution, and image synthesis. The authors aim to provide a comprehensive yet concise introduction to the field, offering pointers to core references, educational resources, and open-source code for those interested in contributing to this rapidly evolving area.