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Pattern Recognition

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


Neural Net Pattern RecognitionClassify data by training a two-layer feed-forward network


AutoencoderAutoencoder class


expand all

nnstartNeural network getting started GUI
viewView neural network
trainAutoencoderTrain an autoencoder
trainSoftmaxLayerTrain a softmax layer for classification
decodeDecode encoded data
encodeEncode input data
predictReconstruct the inputs using trained autoencoder
stackStack encoders from several autoencoders together
networkConvert Autoencoder object into network object
patternnetPattern recognition network
lvqnetLearning vector quantization neural network
trainTrain neural network
trainlmLevenberg-Marquardt backpropagation
trainbrBayesian regularization backpropagation
trainscgScaled conjugate gradient backpropagation
trainrpResilient backpropagation
mseMean squared normalized error performance function
regressionLinear regression
rocReceiver operating characteristic
plotconfusionPlot classification confusion matrix
ploterrhistPlot error histogram
plotperformPlot network performance
plotregressionPlot linear regression
plotrocPlot receiver operating characteristic
plottrainstatePlot training state values
crossentropyNeural network performance
genFunctionGenerate MATLAB function for simulating neural network

Examples and How To

Basic Design

Classify Patterns with a Shallow Neural Network

Use a neural network for classification.

Deploy Trained Neural Network Functions

Simulate and deploy trained neural networks using MATLAB® tools.

Deploy Training of Neural Networks

Learn how to deploy training of a network.

Training Scalability and Efficiency

Neural Networks with Parallel and GPU Computing

Use parallel and distributed computing to speed up neural network training and simulation and handle large data.

Automatically Save Checkpoints During Neural Network Training

Save intermediate results to protect the value of long training runs.

Optimal Solutions

Representing Unknown or Don't-Care Targets

Choose Neural Network Input-Output Processing Functions

Preprocess inputs and targets for more efficient training.

Configure Neural Network Inputs and Outputs

Learn how to manually configure the network before training using the configure function.

Divide Data for Optimal Neural Network Training

Use functions to divide the data into training, validation, and test sets.

Choose a Multilayer Neural Network Training Function

Comparison of training algorithms on different problem types.

Improve Neural Network Generalization and Avoid Overfitting

Learn methods to improve generalization and prevent overfitting.

Train Neural Networks with Error Weights

Learn how to use error weighting when training neural networks.

Normalize Errors of Multiple Outputs

Learn how to fit output elements with different ranges of values.

Deep Networks

Construct Deep Network Using Autoencoders

Illustration of using autoencoders to construct and train a deep network for image classification


Workflow for Neural Network Design

Learn the primary steps in a neural network design process.

Four Levels of Neural Network Design

Learn the different levels of using Neural Network Toolbox functionality.

Multilayer Neural Networks and Backpropagation Training

Workflow for designing a multilayer feedforward neural network for function fitting and pattern recognition.

Multilayer Neural Network Architecture

Learn the architecture of a multilayer neural network.

Understanding Neural Network Toolbox Data Structures

Learn how the format of input data structures affects the simulation of networks.

Neural Network Object Properties

Learn properties that define the basic features of a network.

Neural Network Subobject Properties

Learn properties that define network details such as inputs, layers, outputs, targets, biases, and weights.

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