Learn how GPU Coder produces high-performance CUDA code automatically from a high-level algorithm description in MATLAB. Write your deep learning application with the expressive power of MATLAB, which allows you to describe not just the use of your trained deep learning model in inference mode but also perform data-augmentation and post-processing of the results to create a complete deployment-ready application. GPU Coder can then generate optimized inference code for the whole application. The deep learning inference model is compiled down to TensorRT, while the rest of the application logic is parallelized through creation of CUDA kernels and integration with other CUDA optimized libraries like cuBLAS, cuFFT, etc. The generated code can be cross-compiled to any NVIDIA GPU device that supports TensorRT. This allows engineers and scientists to unlock the expressive ease-of-use of the MATLAB programming language while unleashing deep learning performance by leveraging TensorRT.