Pattern recognition

Addestrare una rete neurale a generalizzare partendo da input di esempio e dalle loro classi, addestrare autoencoder

App

Risolve il problema del pattern recognition utilizzando reti feed-forward a due livelli

Autoencoder Autoencoder class

`nprtool`	Apre l’app Neural Net Pattern Recognition
`view`	Visualizzazione della rete neurale superficiale

`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`	Genera una rete di pattern recognition
`lvqnet`	Learning vector quantization neural network

`train`	Addestra una rete neurale superficiale
`trainlm`	Retropropagazione di Levenberg-Marquardt
`trainbr`	Bayesian regularization backpropagation
`trainscg`	Retropropagazione a gradiente coniugato scalato
`trainrp`	Resilient backpropagation
`mse`	Funzione di performance dell’errore quadratico medio normalizzato
`roc`	Receiver operating characteristic
`plotconfusion`	Plot classification confusion matrix
`ploterrhist`	Plot error histogram
`plotperform`	Traccia la performance della rete
`plotregression`	Traccia la regressione lineare
`plotroc`	Traccia la caratteristica operativa del ricevitore
`plottrainstate`	Traccia i valori dello stato di addestramento
`crossentropy`	Neural network performance
`genFunction`	Generate MATLAB function for simulating shallow neural network

Pattern recognition con una rete neurale superficiale
Utilizzare una rete neurale superficiale per il 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 per la progettazione delle reti neurali
Apprendere le fasi principali del processo di progettazione di una rete neurale.
Four Levels of Neural Network Design
Learn the different levels of using neural network functionality.
Reti neurali superficiali multilivello e addestramento sulla retropropagazione
Workflow per la progettazione di una rete neurale superficiale multilivello feed-forward per l'adattamento di funzioni e il riconoscimento di pattern.
Architettura della rete neurale superficiale multilivello
Apprendere l'architettura di una rete neurale superficiale multilivello.
Understanding Shallow Network Data Structures
Learn how the format of input data structures affects the simulation of networks.
Set di dati campione per reti neurali superficiali
Elenco di set di dati campione da utilizzare negli esperimenti con reti neurali superficiali.
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.