This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Deep Learning Toolbox

Create, analyze, and train deep learning networks

Deep Learning Toolbox™ provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. Apps and plots help you visualize activations, edit network architectures, and monitor training progress.

For small training sets, you can perform transfer learning with pretrained deep network models (including SqueezeNet, Inception-v3, ResNet-101, GoogLeNet, and VGG-19) and models imported from TensorFlow®-Keras and Caffe.

To speed up training on large datasets, you can distribute computations and data across multicore processors and GPUs on the desktop (with Parallel Computing Toolbox™), or scale up to clusters and clouds, including Amazon EC2® P2, P3, and G3 GPU instances (with MATLAB® Distributed Computing Server™).

Getting Started

Learn the basics of Deep Learning Toolbox

Deep Learning with Images

Train convolutional neural networks from scratch or use pretrained networks to quickly learn new tasks

Deep Learning with Time Series, Sequences, and Text

Create and train networks for time series classification, regression, and forecasting tasks

Deep Learning Tuning and Visualization

Plot training progress, assess accuracy, make predictions, tune training options, and visualize features learned by a network

Deep Learning in Parallel and in the Cloud

Scale up deep learning with multiple GPUs locally or in the cloud and train multiple networks interactively or in batch jobs

Deep Learning Applications

Extend deep learning workflows with computer vision, image processing, automated driving, and signals

Deep Learning Import, Export, and Customization

Import and export networks and define custom deep learning layers and datastores

Deep Learning Code Generation

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

Function Approximation and Clustering

Perform regression, classification, and clustering using shallow neural networks

Time Series and Control Systems

Model nonlinear dynamic systems using shallow networks; make predictions using sequential data.