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

Neural Net Pattern Recognition | Classify data by training a two-layer feed-forward network |

`Autoencoder` |
Autoencoder class |

`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` |
Pattern recognition network |

`lvqnet` |
Learning vector quantization neural network |

`train` |
Train 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 |

`regression` |
Linear regression |

`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 neural network |

**Classify Patterns with a 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**

Use MATLAB Runtime to deploy functions that can train a model.

**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.

**Representing Unknown or Don't-Care Targets**

Prevent unknown target values from impacting training.

**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.

**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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