MXNet is a multi-language machine learning library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It offers auto differentiation to derive gradients. MXNet is computation and memory efficient and runs on various heterogeneous systems. The MXNet R package brings flexible and efficient GPU computing and state-of-art deep learning to R. It enables users to write seamless tensor/matrix computation with multiple GPUs in R. It also enables users to construct and customize the state-of-art deep learning models in R, and apply them to tasks such as image classification and data science challenges. Due to the portable design, the MXNet R package can be installed and used on all operating systems supporting R, including Linux, Mac and Windows. In this talk I will provide an overview of the MXNet platform. With demos of state-of-art deep learning models, users can build and modify deep neural networks according to their own need easily. At the same time, the GPU backend will ensure the efficiency of all computing work.