deep learning with gpu coder -凯发k8网页登录
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 a 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 by 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 enable you to use gpu coder for deep learning, without expert knowledge on neural networks, deep learning, or advanced computer vision algorithms.
apps
functions
objects
model settings
topics
matlab
create aseriesnetwork
,dagnetwork
,yolov2objectdetector
,ssdobjectdetector
, ordlnetwork
object for code generation.
generate code for pretrained convolutional neural networks by using the cudnn library.
generate code for pretrained convolutional neural networks by using the tensorrt library.
generate c code for prediction from a deep learning network targeting an arm mali gpu processor.
analyze and optimize the performance of the generated cuda code for deep learning networks.
perform post code generation updates of deep learning network parameters.
fundamental data layout considerations for authoring example main functions.
understand effects of quantization and how to visualize dynamic ranges of network convolution layers.
quantize and generate code for a pretrained convolutional neural network.- lane detection optimized with gpu coder
develop a deep learning lane detection application that runs on nvidia gpus.
generate cuda mex for a traffic sign detection and recognition application that uses deep learning.
generate code and classify an input image into 32 logo categories.- code generation for semantic segmentation network that uses u-net
generate cuda code for the u-net deep learning network for image segmentation.
code generation for thesegnet
image segmentation network.
generate cuda mex from matlab code and denoise grayscale images by using the denoising convolutional neural network.
simulink
- gpu code generation for deep learning networks using matlab function block
simulate and generate code for deep learning models in simulink using matlab function blocks.
simulate and generate code for deep learning models in simulink using library blocks.- targeting nvidia embedded boards
build and deploy to nvidia gpu boards.