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Neural Network Toolbox

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) and autoencoders for image classification, regression, and feature learning.

For small training sets, you can quickly apply deep learning by performing transfer learning with pretrained deep networks. To speed up training on large datasets, you can use Parallel Computing Toolbox™ to distribute computations and data across multicore processors and GPUs on the desktop, and you can scale up to clusters and clouds (including Amazon EC2® P2 GPU instances) with MATLAB® Distributed Computing Server™.

Getting Started

Learn the basics of Neural Network Toolbox

Deep Learning

Construct and train convolutional neural networks (CNNs, ConvNets) for classification and regression and autoencoder neural networks for learning features

Function Approximation and Nonlinear Regression

Create a neural network to generalize nonlinear relationships between example inputs and outputs

Pattern Recognition and Classification

Train a neural network to generalize from example inputs and their classes, construct a deep network using autoencoders


Discover natural distributions, categories, and category relationships

Time Series and Dynamic Systems

Model nonlinear dynamic systems; make predictions using sequential data

Neural Network Control Systems

Control nonlinear systems using model-predictive, NARMA-L2, and model-reference neural networks

Define Neural Network Architectures

Define new neural network architectures and algorithms for advanced applications