Create, train, and simulate shallow and deep learning neural networks

Neural Network Toolbox™ provides algorithms, pretrained models, and apps to create, train, visualize, and simulate both shallow and deep neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.

Deep learning networks include convolutional neural networks (ConvNets, CNNs), directed acyclic graph (DAG) network topologies, and autoencoders for image classification, regression, and feature learning. For time-series classification and prediction, the toolbox provides long short-term memory (LSTM) deep learning networks. You can visualize intermediate layers and activations, modify network architecture, and monitor training progress.

For small training sets, you can quickly apply deep learning by performing transfer learning with pretrained deep network models (GoogLeNet, AlexNet, VGG-16, and VGG-19) and models from the Caffe Model Zoo.

To speed up training on large data sets, 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 GPU instances (with MATLAB Distributed Computing Server™ ).


Capabilities

Deep Learning Networks and Algorithms

Train CNNs, LSTM networks, and autoencoders for image classification, regression, and feature learning.

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Deep Learning Training, Pretrained Models, and Visualization

Perform transfer learning with pretrained deep network models and models from Keras and the Caffe Model Zoo.

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Accelerated Training with GPUs and Large Data Sets

Speed up neural network training and simulation of large data sets.

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Classification, Regression, and Clustering of Shallow Networks

Create, train, and simulate shallow neural networks.

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Shallow Network Architectures

Use a variety of supervised and unsupervised network architectures.

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Training Algorithms

Automatically adjust a shallow network's weights and biases using training and learning functions.

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Preprocessing, Postprocessing, and Improving Generalization

Improve the efficiency of shallow neural network training.

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Code Generation and Deployment

Deploy a trained network to production.

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Product Resources

Discover more about Neural Network Toolbox by exploring these resources.

Documentation

Explore documentation for Neural Network Toolbox functions and features, including release notes and examples.

Functions

Browse the list of available Neural Network Toolbox functions.

System Requirements

View system requirements for the latest release of Neural Network Toolbox.

Technical Articles

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User Stories

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Community and Support

Find answers to questions and explore troubleshooting resources.

Apps

Neural Network Toolbox apps enable you to quickly access common tasks through an interactive interface.


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Neural Network Toolbox requires: MATLAB


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