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.
|Generate C/C++ code from MATLAB code|
|Generate code and build static library for Series or DAG Network|
|Load deep learning network model|
|Create deep learning code generation configuration objects|
|Get convolutional neural network layers supported for code generation for a specific deep learning library|
|Parameters to configure deep learning code generation with the CUDA Deep Neural Network library|
|Parameters to configure deep learning code generation with the NVIDIA TensorRT library|
|Configuration parameters for CUDA code generation from MATLAB code by using GPU Coder|
|Create configuration object containing the parameters passed to
Deep Learning in MATLAB (Deep Learning Toolbox)
Discover deep learning capabilities in MATLAB using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.
Learn About Convolutional Neural Networks (Deep Learning Toolbox)
An introduction to convolutional neural networks and how they work in MATLAB.
Pretrained Deep Neural Networks (Deep Learning Toolbox)
Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction.
Deep Learning with Images (Deep Learning Toolbox)
Train convolutional neural networks from scratch or use pretrained networks to quickly learn new tasks
Overview of CUDA code generation workflow for convolutional neural networks.
Networks and layers supported for code generation.
Architecture of the generated CNN class and its methods.
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.
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.
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.
Build and deploy to NVIDIA GPU boards.