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Pattern Recognition

Train a neural network to generalize from example inputs and their classes, train autoencoders

Solve pattern recognition problem using two-layer feed-forward networks

Autoencoder Autoencoder class

`nprtool`	Open Neural Net Pattern Recognition app
`view`	View shallow neural network

`trainAutoencoder`	Train an autoencoder
`trainSoftmaxLayer`	Train a softmax layer for classification
`decode`	Decode encoded data
`encode`	Encode input data
`predict`	Reconstruct the inputs using trained autoencoder
`stack`	Stack encoders from several autoencoders together
`network`	Convert `Autoencoder` object into `network` object

`patternnet`	Generate pattern recognition network
`lvqnet`	Learning vector quantization neural network

`train`	Train shallow neural network
`trainlm`	Levenberg-Marquardt backpropagation
`trainbr`	Bayesian regularization backpropagation
`trainscg`	Scaled conjugate gradient backpropagation
`trainrp`	Resilient backpropagation
`mse`	Mean squared normalized error performance function
`roc`	Receiver operating characteristic
`plotconfusion`	Plot classification confusion matrix
`ploterrhist`	Plot error histogram
`plotperform`	Plot network performance
`plotregression`	Plot linear regression
`plotroc`	Plot receiver operating characteristic
`plottrainstate`	Plot training state values
`crossentropy`	Neural network performance
`genFunction`	Generate MATLAB function for simulating shallow neural network

Pattern Recognition with a Shallow Neural Network
Use a shallow neural network for pattern recognition.
Deploy Shallow Neural Network Functions
Simulate and deploy trained shallow neural networks using MATLAB^® tools.
Deploy Training of Shallow Neural Networks
Learn how to deploy training of shallow neural networks.

Shallow 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.

Choose Neural Network Input-Output Processing Functions
Preprocess inputs and targets for more efficient training.
Configure Shallow 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 Shallow 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.

Crab Classification
This example illustrates using a neural network as a classifier to identify the sex of crabs from physical dimensions of the crab.
Wine Classification
This example illustrates how a pattern recognition neural network can classify wines by winery based on its chemical characteristics.
Cancer Detection
This example shows how to train a neural network to detect cancer using mass spectrometry data on protein profiles.
Character Recognition
This example illustrates how to train a neural network to perform simple character recognition.

Train Stacked Autoencoders for Image Classification
This example shows how to train stacked autoencoders to classify images of digits.

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 functionality.
Multilayer Shallow Neural Networks and Backpropagation Training
Workflow for designing a multilayer shallow feedforward neural network for function fitting and pattern recognition.
Multilayer Shallow Neural Network Architecture
Learn the architecture of a multilayer shallow neural network.
Understanding Shallow Network Data Structures
Learn how the format of input data structures affects the simulation of networks.
Sample Data Sets for Shallow Neural Networks
List of sample data sets to use when experimenting with shallow neural 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.