Neural Network Toolbox™ provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.
The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox™.
Learn the basics of Neural Network Toolbox
Create a neural network to generalize nonlinear relationships between example inputs and outputs
Train a neural network to generalize from example inputs and their classes, construct a deep network using autoencoders
Construct and train convolutional neural networks (CNNs, ConvNets) for classification and autoencoder neural networks for learning features
Discover natural distributions, categories, and category relationships
Model nonlinear dynamic systems; make predictions using sequential data
Control nonlinear systems using model-predictive, NARMA-L2, and model-reference neural networks
Define new neural network architectures and algorithms for advanced applications