Generate MATLAB^{®} code or CUDA^{®} and C++ code and deploy deep learning networks

Use Deep Network Designer to generate MATLAB code to construct and train a network.

Use MATLAB
Coder™ or GPU Coder™ together with Deep Learning
Toolbox™ to generate C++ or CUDA code and deploy convolutional neural networks on embedded
platforms that use Intel^{®}, ARM^{®}, or NVIDIA^{®}
Tegra^{®} processors.

`dlquantizer` | Quantize a deep neural network to 8-bit scaled integer data types |

`dlquantizationOptions` | Options for quantizing a trained deep neural network |

`calibrate` | Simulate and collect ranges of a deep neural network |

`validate` | Quantize and validate a deep neural network |

Deep Network Quantizer | Quantize a deep neural network to 8-bit scaled integer data types |

**Quantization of Deep Neural Networks**

Understand effects of quantization and how to visualize dynamic ranges of network convolution layers.

**Generate MATLAB Code from Deep Network Designer**

Generate MATLAB code to recreate designing and training a network in Deep Network Designer.

**Deep Learning with GPU Coder (GPU Coder)**

Generate CUDA code for deep learning neural networks

**Code Generation for Deep Learning Networks**

This example shows how to perform code generation for an image classification application that uses deep learning.

**Code Generation for a Sequence-to-Sequence LSTM Network**

This example demonstrates how to generate CUDA® code for a long short-term memory (LSTM) network.

**Deep Learning Prediction on ARM Mali GPU**

This example shows how to use the `cnncodegen`

function to generate code for an image classification application that uses deep learning on ARM® Mali GPUs.

**Code Generation for Object Detection by Using YOLO v2**

This example shows how to generate CUDA® MEX for a you only look once (YOLO) v2 object detector.

**Lane Detection Optimized with GPU Coder**

This example shows how to generate CUDA® code from a deep learning network, represented by a `SeriesNetwork`

object.

**Integrating Deep Learning with GPU Coder into Simulink**

This example shows how to integrate the CUDA® code generated for a deep learning network into Simulink®.

**Deep Learning Prediction by Using NVIDIA TensorRT**

This example shows code generation for a deep learning application by using the NVIDIA TensorRT™ library.

**Deep Learning Prediction by Using Different Batch Sizes**

This example demonstrates code generation with batch sizes greater than 1.

**Traffic Sign Detection and Recognition**

This example shows how to generate CUDA® MEX code for a traffic sign detection and recognition application that uses deep learning.

This example shows code generation for a logo classification application that uses deep learning.

This example shows code generation for pedestrian detection application that uses deep learning.

**Code Generation for Denoising Deep Neural Network**

This example shows how to generate CUDA® MEX from MATLAB® code and denoise grayscale images by using the denoising convolutional neural network (DnCNN [1]).

**Code Generation for Semantic Segmentation Network**

This example shows code generation for an image segmentation application that uses deep learning.

**Train and Deploy Fully Convolutional Networks for Semantic Segmentation**

This example shows how to train and deploy a fully convolutional semantic segmentation network on an NVIDIA® GPU by using GPU Coder™.

**Code Generation for Semantic Segmentation Network by Using U-net**

This example shows code generation for an image segmentation application that uses deep learning.

**Deep Learning Prediction on ARM Mali GPU (GPU Coder)**

This example shows how to use the `cnncodegen`

function to generate code for an image classification application that uses deep learning on ARM® Mali GPUs.

**Code Generation for a Sequence-to-Sequence LSTM Network (GPU Coder)**

This example demonstrates how to generate CUDA® code for a long short-term memory (LSTM) network.

**Code Generation for Deep Learning on ARM Targets**

This example shows how to generate and deploy code for prediction on an ARM®-based device without using a hardware support package.

**Code Generation for Deep Learning on Raspberry Pi**

This example shows how to generate and deploy code for prediction on a Raspberry Pi™ by using `codegen`

with the MATLAB Support Package for Raspberry Pi Hardware.

**Deep Learning Prediction with ARM Compute Using cnncodegen**

This example shows how to use `cnncodegen`

to generate code for a Logo classification application that uses deep learning on ARM® processors.

**Deep Learning Prediction with Intel MKL-DNN**

This example shows how to use `codegen`

to generate code for an image classification application that uses deep learning on Intel® processors.

**Generate C++ Code for Object Detection Using YOLO v2 and Intel MKL-DNN**

This example shows how to generate C++ code for the YOLO v2 Object detection network on an Intel® processor.

**Code Generation and Deployment of MobileNet-v2 Network to Raspberry Pi**

This example shows how to generate and deploy C++ code that uses the MobileNet-v2 pretrained network for object prediction.

**Code Generation for Semantic Segmentation Application on Intel CPUs That Uses U-Net (MATLAB Coder)**

Generate a MEX function that performs image segmentation by using the deep learning network U-Net on Intel CPUs.

**Code Generation for LSTM Network on Raspberry Pi (MATLAB Coder)**

Generate code for a pretrained long short-term memory network to predict Remaining Useful Life (RUI) of a machine.

**Cross Compile Deep Learning Code for ARM Neon Targets (MATLAB Coder)**

Generate library or executable code on host computer for deployment on ARM hardware target.

**Load Pretrained Networks for Code Generation (MATLAB Coder)**

Create a `SeriesNetwork`

, `DAGNetwork`

,
`yolov2ObjectDetector`

, or `ssdObjectDetector`

object for code generation.

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

Generate C++ code for deep learning neural networks (requires Deep Learning Toolbox)