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Deep Learning Basics

Discover deep learning capabilities in MATLAB using convolutional neural networks (ConvNets) for classification and regression

Deep learning uses neural networks to learn useful representations of features directly from data. Perform supervised learning with series and directed acyclic graph (DAG) convolutional neural networks (CNNs or ConvNets) for classification and regression. In addition to creating and training a new network, Neural Network Toolbox™ enables you to perform transfer learning using pretrained networks for image classification.

To get started, see Deep Learning in MATLAB.

Deep learning uses neural networks to learn useful representations of features directly from data. If you have labeled data, perform supervised learning with convolutional neural networks (CNNs, ConvNets) for classification, regression, and transfer learning using pretrained networks.

You can train a convolutional neural network on either a CPU, a GPU, or multiple GPUs and/or in parallel. Training on a GPU or in parallel requires the Parallel Computing Toolbox™. Using a GPU requires a CUDA® enabled NVIDIA® GPU with compute capability 3.0 or higher. Specify the training parameters including the execution environment using the trainingOptions function.

To learn more about

Convolutional Neural Network


alexnetPretrained AlexNet convolutional neural network
vgg16Pretrained VGG-16 convolutional neural network
vgg19Pretrained VGG-19 convolutional neural network
googlenetPretrained GoogLeNet convolutional neural network
inceptionv3Pretrained Inception-v3 convolutional neural network
resnet50Pretrained ResNet-50 convolutional neural network
resnet101Pretrained ResNet-101 convolutional neural network
importCaffeNetworkImport pretrained convolutional neural network models from Caffe
importKerasNetworkImport a pretrained Keras network and weights
trainingOptionsOptions for training neural network
trainNetworkTrain neural network for deep learning


Start Here

Deep Learning in MATLAB

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.

Get Started with Transfer Learning

This example shows how to use transfer learning to retrain AlexNet, a pretrained convolutional neural network, to classify a new set of images.

Pretrained Convolutional Neural Networks

Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction.

Learn About Convolutional Neural Networks

An introduction to convolutional neural networks and how they work in MATLAB.

Create Simple Deep Learning Network for Classification

This example shows how to create and train a simple convolutional neural network for deep learning classification.

Deep Learning with Big Data on GPUs and in Parallel

Train deep networks on CPUs, GPUs, clusters, and clouds, and tune options to suit your hardware.

Featured Examples

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