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Deep Learning Code Generation

Generate CUDA® and C++ code and deploy deep learning networks

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.

Topics

Code Generation for Deep Learning Networks

This example demonstrates code generation for an image classification application that uses deep learning.

Lane Detection Optimized with GPU Coder

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

Traffic Sign Detection and Recognition

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

Logo Recognition Network

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

Pedestrian Detection

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

Running an Embedded Application on the NVIDIA Jetson TX2 Developer Kit

This example shows how to generate CUDA® code from a SeriesNetwork object and target the NVIDIA® TX2 board with an external camera.

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.

Object Detection

This example shows how to generate CUDA® code from a SeriesNetwork object created for YOLO architecture trained for classifying the PASCAL dataset.

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 demonstrates code generation for an image segmentation application that uses deep learning.

Featured Examples