Reconocimiento de patrones

Entrene una red neuronal que desea generalizar a partir de entradas de ejemplo y sus clases, y entrene codificadores automáticos

Apps

Resolver un problema de reconocimiento de patrones utilizando redes prealimentadas de dos capas

Autoencoder Clase de codificador automático

`nprtool`	Abrir la app Neural Net Pattern Recognition
`view`	Visualizar una red neuronal superficial

`trainAutoencoder`	Entrenar un codificador automático
`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`	Generar una red de reconocimiento de patrones
`lvqnet`	Learning vector quantization neural network

`train`	Entrenar una red neuronal superficial
`trainlm`	Retropropagación Levenberg-Marquardt
`trainbr`	Retropropagación de regularización bayesiana
`trainscg`	Retropropagación de gradiente conjugado escalado
`trainrp`	Resilient backpropagation
`mse`	Función de rendimiento normalizada de error cuadrático medio
`roc`	Característica de funcionamiento del receptor
`plotconfusion`	Representar una matriz de confusión de clasificación
`ploterrhist`	Representar un histograma de error
`plotperform`	Representar el rendimiento de la red
`plotregression`	Representar una regresión lineal
`plotroc`	Representar la característica de funcionamiento del receptor
`plottrainstate`	Representar valores de estado de entrenamiento
`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.
Dividir datos para realizar un entrenamiento de red neuronal óptimo
Use funciones para dividir los datos en conjuntos de entrenamiento, validación y prueba.
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.

Flujo de trabajo para el diseño de redes neuronales
Aprenda los principales pasos del proceso de diseño de una red neuronal.
Four Levels of Neural Network Design
Learn the different levels of using neural network functionality.
Redes neuronales superficiales multicapa y entrenamiento de retropropagación
Flujo de trabajo para diseñar una red neuronal prealimentada superficial multicapa para el ajuste de funciones y el reconocimiento de patrones.
Arquitectura de red neuronal superficial multicapa
Aprenda la arquitectura de una red neuronal superficial multicapa.
Understanding Shallow Network Data Structures
Learn how the format of input data structures affects the simulation of networks.
Conjuntos de datos de muestra para redes neuronales superficiales
Lista de conjuntos de datos de muestra que se pueden utilizar al experimentar con redes neuronales superficiales.
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.