Generate CUDA^{®} code for deep learning neural networks

Deep learning is a branch of machine learning that teaches computers to do what comes naturally to humans: learn from experience. The learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as model. Deep learning uses convolutional neural networks (CNNs) to learn useful representations of data directly from images. Neural networks combine multiple nonlinear processing layers, using simple elements operating in parallel and inspired by biological nervous systems. Deep learning models are trained using a large set of labeled data and neural network architectures that contain many layers, usually including some convolutional layers.

You can use GPU Coder™ in tandem with the Deep Learning
Toolbox™ to generate code and deploy CNN on multiple embedded platforms
that use NVIDIA^{®} or ARM^{®} GPU processors. The Deep Learning
Toolbox provides simple MATLAB^{®} commands for creating and interconnecting the layers of a deep
neural network. The availability of pretrained networks and examples such as
image recognition, and driver assistance applications make it easy to use
GPU Coder for deep learning, even without expert knowledge on neural
networks, deep learning, or advanced computer vision algorithms.

GPU Coder | Generate GPU code from MATLAB code |

Check GPU Install | Verify and set up the GPU code generation environment |

`codegen` | Generate C/C++ code from MATLAB code |

`cnncodegen` | Generate code and build static library for Series or DAG Network |

`coder.loadDeepLearningNetwork` | Load deep learning network model |

`coder.DeepLearningConfig` | Create deep learning code generation configuration objects |

`coder.MklDNNConfig` | Parameters to configure deep learning code generation with the Intel Math Kernel Library for Deep Neural Networks |

`coder.CuDNNConfig` | Parameters to configure deep learning code generation with the CUDA Deep Neural Network library |

`coder.TensorRTConfig` | Parameters to configure deep learning code generation with the NVIDIA TensorRT library |

`coder.getDeepLearningLayers` | Get convolutional neural network layers supported for code generation for a specific deep learning library |

`gpucoderexamples` | Product examples |

`coder.gpuConfig` | Configuration parameters for CUDA code generation from MATLAB code with GPU Coder |

`coder.gpuEnvConfig` | Create configuration object containing the parameters passed to coder.checkGpuInstall for performing GPU code generation environment checks |

**Deep Learning with MATLAB Coder (MATLAB Coder)**