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Deep Learning Toolbox Funciones - Lista alfabética

A

`AcceleratedFunction`	Accelerated deep learning function (desde R2021a)
`accuracyMetric`	Deep learning accuracy metric (desde R2023b)
`activations`	(No recomendado) Calcular las activaciones de las capas de una red de deep learning
`adamupdate`	Update parameters using adaptive moment estimation (Adam) (desde R2019b)
`adapt`	Adaptar una red neuronal a los datos mientras se simula
`adaptiveAveragePooling2dLayer`	Adaptive average pooling 2-D layer (desde R2024a)
`adaptwb`	Adaptar una red con reglas de aprendizaje de pesos y sesgos
`adddelay`	Add delay to neural network response
`addInputLayer`	Add input layer to network (desde R2022b)
`additionLayer`	Capa de suma
`addLayers`	Añadir capas a una red neuronal
`addMetrics`	Compute additional classification performance metrics (desde R2022b)
`addParameter`	Add parameter to `ONNXParameters` object (desde R2020b)
`alexnet`	(No recomendado) Red neuronal convolucional AlexNet
`analyzeNetwork`	Analyze deep learning network architecture
`assembleNetwork`	(No recomendado) Ensamblar una red de deep learning a partir de capas preentrenadas
`attention`	Dot-product attention (desde R2022b)
`attentionLayer`	Dot-product attention layer (desde R2024a)
`aucMetric`	Deep learning area under ROC curve (AUC) metric (desde R2023b)
`audioDataAugmenter`	Augment audio data (desde R2019b)
`audioDatastore`	Datastore for collection of audio files
`audioFeatureExtractor`	Streamline audio feature extraction (desde R2019b)
`audioPretrainedNetwork`	Pretrained audio neural networks (desde R2024a)
`augment`	Apply identical random transformations to multiple images
`augmentedImageDatastore`	Transformar lotes para aumentar datos de imágenes
`augmentedImageSource`	(To be removed) Generate batches of augmented image data
`Autoencoder`	Clase de codificador automático
`average`	Compute performance metrics for average receiver operating characteristic (ROC) curve in multiclass problem (desde R2022b)
`averagePooling1dLayer`	1-D average pooling layer (desde R2021b)
`averagePooling2dLayer`	Average pooling layer
`averagePooling3dLayer`	3-D average pooling layer
`avgpool`	Pool data to average values over spatial dimensions (desde R2019b)

B

`BaselineDistributionDiscriminator`	Baseline distribution discriminator (desde R2023a)
`batchnorm`	Normalize data across all observations for each channel independently (desde R2019b)
`batchNormalizationLayer`	Batch normalization layer
`bilstmLayer`	Bidirectional long short-term memory (BiLSTM) layer for recurrent neural network (RNN)
`blockedImageDatastore`	Datastore for use with blocks from `blockedImage` objects (desde R2021a)
`boxdist`	Distance between two position vectors
`boxLabelDatastore`	Datastore for bounding box label data (desde R2019b)
`bttderiv`	Backpropagation through time derivative function

C

`calibrate`	Simulate and collect ranges of a deep neural network (desde R2020a)
`cascadeforwardnet`	Generar una red neuronal prealimentada en cascada
`catelements`	Concatenate neural network data elements
`catsamples`	Concatenate neural network data samples
`catsignals`	Concatenate neural network data signals
`cattimesteps`	Concatenate neural network data timesteps
`cellmat`	Crear un arreglo de celdas de matrices
`cellpose`	Configure Cellpose model for cell segmentation (desde R2023b)
`checkLayer`	Check validity of custom or function layer
`classificationLayer`	(No recomendado) Capa de clasificación de salida
`ClassificationOutputLayer`	(Not recommended) Classification output layer
`classify`	(No recomendado) Clasificar datos con una red neuronal de deep learning entrenada
`classifyAndUpdateState`	(Not recommended) Classify data using a trained recurrent neural network and update the network state
`classifySound`	Classify sounds in audio signal (desde R2020b)
`clearCache`	Clear accelerated deep learning function trace cache (desde R2021a)
`clippedReluLayer`	Capa de unidad lineal rectificada (ReLU) recortada
`close`	Close training information plot (desde R2023b)
`closeloop`	Convertir la retroalimentación de lazo abierto de una red neuronal en una de lazo cerrado
`codegen`	Generate C/C++ code from MATLAB code
`coder.DeepLearningConfig`	Create deep learning code generation configuration objects
`coder.getDeepLearningLayers`	Get the list of layers supported for code generation for a specific deep learning library
`coder.loadDeepLearningNetwork`	Load deep learning network model
`coder.loadNetworkDistributionDiscriminator`	Load network distribution discriminator for code generation (desde R2023a)
`combine`	Combine data from multiple datastores
`CombinedDatastore`	Datastore to combine data read from multiple underlying datastores
`combvec`	Crear todas las combinaciones de vectores
`compet`	Función de transferencia competitiva
`competlayer`	Capa competitiva
`compressNetworkUsingProjection`	Compress neural network using projection (desde R2022b)
`con2seq`	Convertir vectores concurrentes en vectores secuenciales
`concatenationLayer`	Capa de concatenación
`concur`	Create concurrent bias vectors
`configure`	Configurar las entradas y las salidas de la red para adaptarlas mejor a los datos de entrada y los datos objetivo
`confusion`	Matriz de confusión de clasificación
`confusionchart`	Crear una gráfica de matriz de confusión para un problema de clasificación
`confusionmat`	Calcular la matriz de confusión para un problema de clasificación
`connectLayers`	Conectar capas en una red neuronal
`convolution1dLayer`	Capa convolucional 1D (desde R2021b)
`convolution2dLayer`	2-D convolutional layer
`convolution3dLayer`	3-D convolutional layer
`convwf`	Función de peso de convolución
`countlabels`	Count number of unique labels (desde R2021a)
`crop2dLayer`	2-D crop layer
`crop3dLayer`	3-D crop layer (desde R2019b)
`crosschannelnorm`	Cross channel square-normalize using local responses (desde R2020a)
`crossChannelNormalizationLayer`	Channel-wise local response normalization layer
`crossentropy`	Cross-entropy loss for classification tasks (desde R2019b)
`crossentropy`	Neural network performance
`ctc`	Connectionist temporal classification (CTC) loss for unaligned sequence classification (desde R2021a)
`cwtfilterbank`	Continuous wavelet transform filter bank
`cwtLayer`	Continuous wavelet transform (CWT) layer (desde R2022b)
`cwtmag2sig`	Signal reconstruction from CWT magnitude (desde R2023b)

D

`dag2dlnetwork`	Convert `SeriesNetwork` and `DAGNetwork` to `dlnetwork` (desde R2024a)
`DAGNetwork`	(No recomendado) Red gráfica acíclica dirigida (DAG) para deep learning
`darknet19`	(Not recommended) DarkNet-19 convolutional neural network (desde R2020a)
`darknet53`	(No recomendado) Red neuronal convolucional DarkNet-53 (desde R2020a)
`decode`	Decode encoded data
`deepDreamImage`	Visualize network features using deep dream
`deeplabv3plus`	Create DeepLab v3+ convolutional neural network for semantic image segmentation (desde R2024a)
`deepSignalAnomalyDetector`	Create signal anomaly detector (desde R2023a)
`defaultderiv`	Función derivada predeterminada
`densenet201`	(No recomendado) Red neuronal convolucional DenseNet-201
`depthConcatenationLayer`	Capa de concatenación de profundidad
`detect`	Detect objects using PointPillars object detector (desde R2021b)
`detectspeechnn`	Detect boundaries of speech in audio signal using AI (desde R2023a)
`detectTextCRAFT`	Detect texts in images by using CRAFT deep learning model (desde R2022a)
`dims`	Etiquetas de dimensión de `dlarray` (desde R2019b)
`disconnectLayers`	Disconnect layers in neural network
`dist`	Función de peso de distancia euclidiana
`distdelaynet`	Distributed delay network
`distributionScores`	Distribution confidence scores (desde R2023a)
`divideblock`	Dividir objetivos en tres conjuntos usando bloques de índices
`divideind`	Divide targets into three sets using specified indices
`divideint`	Dividir objetivos en tres conjuntos usando índices intercalados
`dividerand`	Dividir objetivos en tres conjuntos usando índices aleatorios
`dividetrain`	Asignar todos los objetivos a un conjunto de entrenamiento
`dlaccelerate`	Accelerate deep learning function for custom training loops (desde R2021a)
`dlarray`	Arreglo de deep learning para personalización (desde R2019b)
`dlconv`	Deep learning convolution (desde R2019b)
`dlcwt`	Deep learning continuous wavelet transform (desde R2022b)
`dlfeval`	Evaluate deep learning model for custom training loops (desde R2019b)
`dlgradient`	Compute gradients for custom training loops using automatic differentiation (desde R2019b)
`dlhdl.Target`	Configure interface to target board for workflow deployment (desde R2020b)
`dlhdl.Workflow`	Configure deployment workflow for deep learning neural network (desde R2020b)
`dlistft`	Deep learning inverse short-time Fourier transform (desde R2024a)
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (desde R2022a)
`dlmtimes`	(Not recommended) Batch matrix multiplication for deep learning (desde R2020a)
`dlnetwork`	Redes neuronales de deep learning (desde R2019b)
`dlode45`	Deep learning solution of nonstiff ordinary differential equation (ODE) (desde R2021b)
`dlquantizationOptions`	Options for quantizing a trained deep neural network (desde R2020a)
`dlquantizer`	Quantize a deep neural network to 8-bit scaled integer data types (desde R2020a)
`dlstft`	Deep learning short-time Fourier transform (desde R2021a)
`dltranspconv`	Deep learning transposed convolution (desde R2019b)
`dlupdate`	Update parameters using custom function (desde R2019b)
`doc2sequence`	Convert documents to sequences for deep learning
`dotprod`	Función de peso de producto de puntos
`drise`	Explain object detection network predictions using D-RISE (desde R2024a)
`dropoutLayer`	Capa de abandono

E

`edfheader`	Create header structure for EDF or EDF+ file (desde R2021a)
`edfinfo`	Get information about EDF/EDF+ file (desde R2020b)
`edfread`	Leer datos del archivo EDF/EDF+ (desde R2020b)
`edfwrite`	Create or modify EDF or EDF+ file (desde R2021a)
`efficientnetb0`	(No recomendado) Red neuronal convolucional EfficientNet-b0 (desde R2020b)
`elliot2sig`	Elliot 2 symmetric sigmoid transfer function
`elliotsig`	Elliot symmetric sigmoid transfer function
`elmannet`	Elman neural network
`eluLayer`	Exponential linear unit (ELU) layer
`embed`	Embed discrete data (desde R2020b)
`embeddingConcatenationLayer`	Embedding concatenation layer (desde R2023b)
`encode`	Encode input data
`EnergyDistributionDiscriminator`	Energy distribution discriminator (desde R2023a)
`equalizeLayers`	Equalize layer parameters of deep neural network (desde R2022b)
`errsurf`	Error surface of single-input neuron
`estimateNetworkMetrics`	Estimate network metrics for specific layers of a neural network (desde R2022a)
`estimateNetworkOutputBounds`	Estimate output bounds of deep learning network (desde R2022b)
`expandLayers`	Expand network layers (desde R2024a)
`experiments.Monitor`	Update results table and training plots for custom training experiments (desde R2021a)
`exportNetworkToTensorFlow`	Export Deep Learning Toolbox network to TensorFlow (desde R2022b)
`exportONNXNetwork`	Export network to ONNX model format
`extendts`	Extend time series data to given number of timesteps
`extractdata`	Extraer datos de `dlarray` (desde R2019b)

F

`fasterRCNNObjectDetector`	Detect objects using Faster R-CNN deep learning detector
`fastFlowAnomalyDetector`	Detect anomalies using FastFlow network (desde R2023a)
`fastRCNNObjectDetector`	Detect objects using Fast R-CNN deep learning detector
`fastTextWordEmbedding`	Pretrained fastText word embedding
`fcddAnomalyDetector`	Detect anomalies using fully convolutional data description (FCDD) network for anomaly detection (desde R2022b)
`featureInputLayer`	Capa de entrada de características (desde R2020b)
`feedforwardnet`	Generar una red neuronal prealimentada
`filenames2labels`	Get list of labels from filenames (desde R2022b)
`findchangepts`	Find abrupt changes in signal
`finddim`	Find dimensions with specified label (desde R2019b)
`findpeaks`	Encontrar los máximos locales
`findPlaceholderLayers`	Find placeholder layers in network architecture imported from Keras or ONNX
`fitnet`	Red neuronal de ajuste de funciones
`fixunknowns`	Process data by marking rows with unknown values
`flattenLayer`	Capa aplanada
`folders2labels`	Get list of labels from folder names (desde R2021a)
`formwb`	Formar sesgos y pesos en un único vector
`forward`	Compute deep learning network output for training (desde R2019b)
`fpderiv`	Enviar hacia adelante la función de derivada
`freezeParameters`	Convert learnable network parameters in `ONNXParameters` to nonlearnable (desde R2020b)
`fromnndata`	Convert data from standard neural network cell array form
`fScoreMetric`	Deep learning F-score metric (desde R2023b)
`fullyconnect`	Sum all weighted input data and apply a bias (desde R2019b)
`fullyConnectedLayer`	Capa totalmente conectada
`functionLayer`	Function layer (desde R2021b)
`functionToLayerGraph`	(To be removed) Convert deep learning model function to a layer graph (desde R2019b)

G

`gadd`	Generalized addition
`gdivide`	Generalized division
`gelu`	Apply Gaussian error linear unit (GELU) activation (desde R2022b)
`geluLayer`	Gaussian error linear unit (GELU) layer (desde R2022b)
`generateFunction`	Generate a MATLAB function to run the autoencoder
`generateSimulink`	Generate a Simulink model for the autoencoder
`genFunction`	Generate MATLAB function for simulating shallow neural network
`gensim`	Generar un bloque de Simulink para la simulación de redes neuronales superficiales
`getelements`	Get neural network data elements
`getL2Factor`	Get L₂ regularization factor of layer learnable parameter
`getLayer`	Look up a layer by name or path (desde R2024a)
`getLearnRateFactor`	Get learn rate factor of layer learnable parameter
`getsamples`	Get neural network data samples
`getsignals`	Get neural network data signals
`getsiminit`	Get Simulink neural network block initial input and layer delays states
`gettimesteps`	Get neural network data timesteps
`getwb`	Obtener los valores de peso y sesgo de la red como un vector único
`globalAveragePooling1dLayer`	1-D global average pooling layer (desde R2021b)
`globalAveragePooling2dLayer`	2-D global average pooling layer (desde R2019b)
`globalAveragePooling3dLayer`	3-D global average pooling layer (desde R2019b)
`globalMaxPooling1dLayer`	1-D global max pooling layer (desde R2021b)
`globalMaxPooling2dLayer`	Global max pooling layer (desde R2020a)
`globalMaxPooling3dLayer`	3-D global max pooling layer (desde R2020a)
`gmultiply`	Multiplicación generalizada
`gnegate`	Negación generalizada
`googlenet`	(No recomendado) Red neuronal convolucional GoogLeNet
`gpu2nndata`	Reformat neural data back from GPU
`gradCAM`	Explain network predictions using Grad-CAM (desde R2021a)
`gridtop`	Grid layer topology function
`groupedConvolution2dLayer`	2-D grouped convolutional layer
`groupLayers`	Group layers into network layers (desde R2024a)
`groupnorm`	Normalize data across grouped subsets of channels for each observation independently (desde R2020b)
`groupNormalizationLayer`	Group normalization layer (desde R2020b)
`groupSubPlot`	Group metrics in experiment training plot (desde R2021a)
`groupSubPlot`	Group metrics in training plot (desde R2022b)
`gru`	Gated recurrent unit (desde R2020a)
`gruLayer`	Gated recurrent unit (GRU) layer for recurrent neural network (RNN) (desde R2020a)
`gruProjectedLayer`	Gated recurrent unit (GRU) projected layer for recurrent neural network (RNN) (desde R2023b)
`gsqrt`	Generalized square root
`gsubtract`	Resta generalizada

H

`hardlim`	Función de transferencia de límite estricto
`hardlims`	Función de transferencia de límite estricto simétrica
`hasdata`	Determine if minibatchqueue can return mini-batch (desde R2020b)
`HBOSDistributionDiscriminator`	HBOS distribution discriminator (desde R2023a)
`hextop`	Hexagonal layer topology function
`huber`	Huber loss for regression tasks (desde R2021a)

I

`image3dInputLayer`	3-D image input layer
`imageDataAugmenter`	Configurar el aumento de datos de imagen
`imageDatastore`	Datastore for image data
`imageInputLayer`	Capa de entrada de imagen
`imageLIME`	Explain network predictions using LIME (desde R2020b)
`imagePretrainedNetwork`	Pretrained neural network for images (desde R2024a)
`importCaffeLayers`	Import convolutional neural network layers from Caffe
`importCaffeNetwork`	Import pretrained convolutional neural network models from Caffe
`importKerasLayers`	(To be removed) Import layers from Keras network
`importKerasNetwork`	(To be removed) Import pretrained Keras network and weights
`importNetworkFromONNX`	Import ONNX network as MATLAB network (desde R2023b)
`importNetworkFromPyTorch`	Import PyTorch network as MATLAB network (desde R2022b)
`importNetworkFromTensorFlow`	Import TensorFlow network as MATLAB network (desde R2023b)
`importONNXFunction`	Import pretrained ONNX network as a function (desde R2020b)
`importONNXLayers`	(To be removed) Import layers from ONNX network
`importONNXNetwork`	(To be removed) Import pretrained ONNX network
`importTensorFlowLayers`	(To be removed) Import layers from TensorFlow network (desde R2021a)
`importTensorFlowNetwork`	(To be removed) Import pretrained TensorFlow network (desde R2021a)
`inceptionresnetv2`	(No recomendado) Red neuronal convolucional Inception-ResNet-v2 preentrenada
`inceptionv3`	(No recomendado) Red neuronal convolucional Inception-v3
`ind2vec`	Convertir índices en vectores
`ind2word`	Map encoding index to word
`indexing1dLayer`	1-D indexing layer (desde R2023b)
`init`	Inicializar una red neuronal
`initcon`	Conscience bias initialization function
`initialize`	Initialize learnable and state parameters of a `dlnetwork` (desde R2021a)
`initlay`	Función de inicialización de red de capa a capa
`initlvq`	LVQ weight initialization function
`initnw`	Nguyen-Widrow layer initialization function
`initwb`	By weight and bias layer initialization function
`initzero`	Zero weight and bias initialization function
`inputLayer`	Input layer (desde R2023b)
`instancenorm`	Normalize across each channel for each observation independently (desde R2021a)
`instanceNormalizationLayer`	Instance normalization layer (desde R2021a)
`isconfigured`	Indicate if network inputs and outputs are configured
`isdlarray`	Check if object is `dlarray` (desde R2020b)
`isequal`	Check equality of neural networks (desde R2021a)
`isequaln`	Check equality of neural networks ignoring `NaN` values (desde R2021a)
`isInNetworkDistribution`	Determine whether data is within the distribution of the network (desde R2023a)
`istftLayer`	Inverse short-time Fourier transform layer (desde R2024a)
`isVocabularyWord`	Test if word is member of word embedding or encoding

L

`l1loss`	L₁ loss for regression tasks (desde R2021b)
`l2loss`	L₂ loss for regression tasks (desde R2021b)
`labeledSignalSet`	Create labeled signal set
`Layer`	Capa de red para deep learning
`layerGraph`	(No recomendado) Gráfica de capas de red de deep learning
`layernorm`	Normalize data across all channels for each observation independently (desde R2021a)
`layerNormalizationLayer`	Layer normalization layer (desde R2021a)
`layrecnet`	Red neuronal recurrente de capas
`lbfgsState`	State of limited-memory BFGS (L-BFGS) solver (desde R2023a)
`lbfgsupdate`	Update parameters using limited-memory BFGS (L-BFGS) (desde R2023a)
`leakyrelu`	Apply leaky rectified linear unit activation (desde R2019b)
`leakyReluLayer`	Capa de unidad lineal rectificada (ReLU) con fugas
`learncon`	Conscience bias learning function
`learngd`	Gradient descent weight and bias learning function
`learngdm`	Gradient descent with momentum weight and bias learning function
`learnh`	Hebb weight learning rule
`learnhd`	Hebb with decay weight learning rule
`learnis`	Instar weight learning function
`learnk`	Kohonen weight learning function
`learnlv1`	LVQ1 weight learning function
`learnlv2`	LVQ2.1 weight learning function
`learnos`	Outstar weight learning function
`learnp`	Perceptron weight and bias learning function
`learnpn`	Normalized perceptron weight and bias learning function
`learnsom`	Self-organizing map weight learning function
`learnsomb`	Batch self-organizing map weight learning function
`learnwh`	Widrow-Hoff weight/bias learning function
`linearlayer`	Crear una capa lineal
`linkdist`	Link distance function
`loadTFLiteModel`	Load TensorFlow Lite model (desde R2022a)
`logsig`	Función de transferencia sigmoide logarítmica
`lstm`	Memoria de corto-largo plazo (desde R2019b)
`lstmLayer`	Long short-term memory (LSTM) layer for recurrent neural network (RNN)
`lstmProjectedLayer`	Long short-term memory (LSTM) projected layer for recurrent neural network (RNN) (desde R2022b)
`lvqnet`	Learning vector quantization neural network
`lvqoutputs`	LVQ outputs processing function

M

`mae`	Función de rendimiento con media de errores absolutos
`mandist`	Función de peso de distancia de Manhattan
`mapminmax`	Procesar matrices mediante la aplicación de valores mínimos y máximos de filas a [`-1 1`]
`mapstd`	Process matrices by mapping each row’s means to 0 and deviations to 1
`maskrcnn`	Detect objects using Mask R-CNN instance segmentation (desde R2021b)
`matlab.io.datastore.BackgroundDispatchable`	(Not recommended) Add prefetch reading support to datastore
`matlab.io.datastore.BackgroundDispatchable.readByIndex`	(Not recommended) Return observations specified by index from datastore
`matlab.io.datastore.MiniBatchable`	Add mini-batch support to datastore
`matlab.io.datastore.MiniBatchable.read`	(Not recommended) Read data from custom mini-batch datastore
`matlab.io.datastore.PartitionableByIndex`	(Not recommended) Add parallelization support to datastore
`matlab.io.datastore.PartitionableByIndex.partitionByIndex`	(Not recommended) Partition datastore according to indices
`maxlinlr`	Maximum learning rate for linear layer
`maxpool`	Pool data to maximum value (desde R2019b)
`maxPooling1dLayer`	1-D max pooling layer (desde R2021b)
`maxPooling2dLayer`	Max pooling layer
`maxPooling3dLayer`	3-D max pooling layer
`maxunpool`	Unpool the output of a maximum pooling operation (desde R2019b)
`maxUnpooling2dLayer`	Max unpooling layer
`meanabs`	Media de los elementos absolutos de una matriz o matrices
`meansqr`	Media del cuadrado de los elementos de una matriz o matrices
`midpoint`	Función de inicialización de pesos midpoint
`minibatchpredict`	Mini-batched neural network prediction (desde R2024a)
`minibatchqueue`	Create mini-batches for deep learning (desde R2020b)
`minmax`	Intervalos de filas de matrices
`mobilenetv2`	(No recomendado) Red neuronal convolucional MobileNet-v2
`modwt`	Maximal overlap discrete wavelet transform
`modwtLayer`	Maximal overlap discrete wavelet transform (MODWT) layer (desde R2022b)
`mse`	Error cuadrático medio dividido (desde R2019b)
`mse`	Función de rendimiento normalizada de error cuadrático medio
`multiplicationLayer`	Multiplication layer (desde R2020b)

N

`narnet`	Red neuronal autorregresiva no lineal
`narxnet`	Red neuronal autorregresiva no lineal con entrada externa
`nasnetlarge`	(No recomendado) Red neuronal convolucional NASNet-Large preentrenada
`nasnetmobile`	(No recomendado) Red neuronal convolucional NASNet-Mobile preentrenada
`nctool`	Abrir la app Neural Net Clustering
`negdist`	Negative distance weight function
`netinv`	Función de transferencia inversa
`netprod`	Product net input function
`netsum`	Sum net input function
`network`	Convert `Autoencoder` object into `network` object
`network`	Crear una red neuronal superficial personalizada
`NetworkAnalysis`	Deep learning network analysis information (desde R2024a)
`networkDataLayout`	Deep learning network data layout for learnable parameter initialization (desde R2022b)
`networkDistributionDiscriminator`	Deep learning distribution discriminator (desde R2023a)
`networkLayer`	Network Layer (desde R2024a)
`neuralODELayer`	Neural ODE layer (desde R2023b)
`neuronPCA`	Principal component analysis of neuron activations (desde R2022b)
`newgrnn`	Diseñar una red neuronal de regresión generalizada
`newlind`	Design linear layer
`newpnn`	Diseñar una red neuronal probabilística
`newrb`	Diseñar una red de base radial
`newrbe`	Diseñar una red de base radial exacta
`next`	Obtener el próximo minilote de datos de minibatchqueue (desde R2020b)
`nftool`	Abrir la app Neural Net Fitting
`nncell2mat`	Combine neural network cell data into matrix
`nncorr`	Cross correlation between neural network time series
`nndata`	Create neural network data
`nndata2gpu`	Format neural data for efficient GPU training or simulation
`nndata2sim`	Convert neural network data to Simulink time series
`nnsize`	Number of neural data elements, samples, timesteps, and signals
`nntool`	(Eliminado) Abrir Network/Data Manager
`nntraintool`	(Eliminada) Herramienta de entrenamiento de redes neuronales
`noloop`	Remove neural network open- and closed-loop feedback
`normc`	Normalizar columnas de una matriz
`normprod`	Normalized dot product weight function
`normr`	Normalizar filas de una matriz
`nprtool`	Abrir la app Neural Net Pattern Recognition
`ntstool`	Abrir la app Neural Net Time Series
`num2deriv`	Numeric two-point network derivative function
`num5deriv`	Numeric five-point stencil neural network derivative function
`numelements`	Number of elements in neural network data
`numfinite`	Number of finite values in neural network data
`numnan`	Number of NaN values in neural network data
`numsamples`	Number of samples in neural network data
`numsignals`	Number of signals in neural network data
`numtimesteps`	Number of time steps in neural network data

O

`occlusionSensitivity`	Explain network predictions by occluding the inputs (desde R2019b)
`ODINDistributionDiscriminator`	ODIN distribution discriminator (desde R2023a)
`onehotdecode`	Decode probability vectors into class labels (desde R2020b)
`onehotencode`	Encode data labels into one-hot vectors (desde R2020b)
`ONNXParameters`	Parameters of imported ONNX network for deep learning (desde R2020b)
`openl3Embeddings`	Extract OpenL3 feature embeddings (desde R2022a)
`openloop`	Convert neural network closed-loop feedback to open loop

P

`paddata`	Pad data by adding elements (desde R2023b)
`padsequences`	Pad or truncate sequence data to same length (desde R2021a)
`partition`	Partition minibatchqueue (desde R2020b)
`partitionByIndex`	Partition `augmentedImageDatastore` according to indices
`patchCoreAnomalyDetector`	Detect anomalies using PatchCore network (desde R2023a)
`patchEmbeddingLayer`	Patch embedding layer (desde R2023b)
`patternnet`	Generar una red de reconocimiento de patrones
`perceptron`	Clasificador binario de una sola capa simple
`perform`	Calcular el rendimiento de la red
`pitchnn`	Estimate pitch with deep learning neural network (desde R2021a)
`pixelLabelDatastore`	Datastore for pixel label data
`PlaceholderLayer`	Layer replacing an unsupported Keras or ONNX layer
`plot`	Representar una arquitectura de red neuronal
`plot`	Plot receiver operating characteristic (ROC) curves and other performance curves (desde R2022b)
`plotconfusion`	Representar una matriz de confusión de clasificación
`plotep`	Plot weight-bias position on error surface
`ploterrcorr`	Plot autocorrelation of error time series
`ploterrhist`	Representar un histograma de error
`plotes`	Plot error surface of single-input neuron
`plotfit`	Representar el ajuste de una función
`plotinerrcorr`	Plot input to error time-series cross-correlation
`plotpc`	Representar la línea de clasificación en la gráfica de vectores del perceptrón
`plotperform`	Representar el rendimiento de la red
`plotpv`	Representar ventores de entrada/objetivo del perceptrón
`plotregression`	Representar una regresión lineal
`plotresponse`	Representar una respuesta de serie de tiempo de red dinámica
`plotroc`	Representar la característica de funcionamiento del receptor
`plotsom`	Plot self-organizing map
`plotsomhits`	Representar los aciertos de muestra de un mapa autoorganizado
`plotsomnc`	Representar conexiones vecinas de mapa autoorganizado
`plotsomnd`	Representar distancias de vecinas de un mapa autoorganizado
`plotsomplanes`	Plot self-organizing map weight planes
`plotsompos`	Representar posiciones de pesos de un mapa autoorganizado
`plotsomtop`	Representar una topología de mapa autoorganizado
`plottrainstate`	Representar valores de estado de entrenamiento
`plotv`	(Se eliminará) Representar vectores como líneas desde el origen
`plotvec`	Representar vectores con diferentes colores
`plotwb`	Plot Hinton diagram of weight and bias values
`plotWeights`	Plot a visualization of the weights for the encoder of an autoencoder
`pnormc`	Pseudonormalize columns of matrix
`pointnetplusLayers`	(Not recommended) Create PointNet++ segmentation network (desde R2021b)
`pointPillarsObjectDetector`	PointPillars object detector (desde R2021b)
`posemaskrcnn`	Predict object pose using Pose Mask R-CNN pose estimation (desde R2024a)
`positionEmbeddingLayer`	Position embedding layer (desde R2023b)
`poslin`	Función de transferencia lineal positiva
`precisionMetric`	Deep learning precision metric (desde R2023b)
`predict`	Compute deep learning network output for inference (desde R2019b)
`predict`	(No recomendado) Predecir respuestas usando una red neuronal de deep learning entrenada
`predict`	Compute deep learning network output for inference by using a TensorFlow Lite model (desde R2022a)
`predict`	Reconstruct the inputs using trained autoencoder
`predictAndUpdateState`	(Not recommended) Predict responses using a trained recurrent neural network and update the network state
`preluLayer`	Parametrized Rectified Linear Unit (PReLU) layer (desde R2024a)
`preparets`	Preparar datos de series de tiempo de entrada y objetivo para simulación o entrenamiento de red
`processpca`	Process columns of matrix with principal component analysis
`ProjectedLayer`	Compressed neural network layer using projection (desde R2023b)
`prune`	Delete neural inputs, layers, and outputs with sizes of zero
`prunedata`	Prune data for consistency with pruned network
`purelin`	Función de transferencia lineal

Q

`quant`	Discretizar valores como múltiplos de cantidad
`quantizationDetails`	Display quantization details for a neural network (desde R2022a)
`quantize`	Quantize deep neural network (desde R2022a)

R

`radbas`	Función de transferencia de base radial
`radbasn`	Función de transferencia de base radial normalizada
`randnc`	Normalized column weight initialization function
`randnr`	Normalized row weight initialization function
`randomPatchExtractionDatastore`	Datastore for extracting random 2-D or 3-D random patches from images or pixel label images
`rands`	Función de inicialización de peso/sesgo aleatoria simétrica
`randsmall`	Small random weight/bias initialization function
`randtop`	Random layer topology function
`rcnnObjectDetector`	Detect objects using R-CNN deep learning detector
`read`	Read data from `augmentedImageDatastore`
`readByIndex`	Read data specified by index from `augmentedImageDatastore`
`readWordEmbedding`	Read word embedding from file
`recallMetric`	Deep learning recall metric (desde R2023b)
`recordMetrics`	Record metric values in experiment results table and training plot (desde R2021a)
`recordMetrics`	Record metric values for custom training loops (desde R2022b)
`regression`	(No recomendado) Realizar una regresión lineal de las salidas de redes superficiales en los objetivos
`regressionLayer`	(No recomendado) Capa de salida de regresión
`RegressionOutputLayer`	Capa de salida de regresión
`reidentificationNetwork`	Re-identification deep learning network for re-identifying and tracking objects (desde R2024a)
`relu`	Aplicar la activación de unidad lineal rectificada (desde R2019b)
`reluLayer`	Capa de unidad lineal rectificada (ReLU)
`removeconstantrows`	Process matrices by removing rows with constant values
`removedelay`	Remove delay to neural network’s response
`removeLayers`	Remove layers from neural network
`removeParameter`	Remove parameter from `ONNXParameters` object (desde R2020b)
`removerows`	Procesar matrices eliminando filas con índices especificados
`replaceLayer`	Replace layer in neural network
`reset`	Reset minibatchqueue to start of data (desde R2020b)
`resetState`	Reset state parameters of neural network
`resize`	Resize data by adding or removing elements (desde R2023b)
`resnet101`	(No recomendado) Red neuronal convolucional ResNet-101
`resnet18`	(No recomendado) Red neuronal convolucional ResNet-18
`resnet3dLayers`	(Not recommended) Create 3-D residual network (desde R2021b)
`resnet3dNetwork`	3-D residual neural network (desde R2024a)
`resnet50`	(No recomendado) Red neuronal convolucional ResNet-50
`resnetLayers`	(Not recommended) Create 2-D residual network (desde R2021b)
`resnetNetwork`	2-D residual neural network (desde R2024a)
`revert`	Change network weights and biases to previous initialization values
`risetime`	Rise time of positive-going bilevel waveform transitions
`rmseMetric`	Deep learning root mean squared error metric (desde R2023b)
`rmspropupdate`	Update parameters using root mean squared propagation (RMSProp) (desde R2019b)
`roc`	Característica de funcionamiento del receptor
`rocmetrics`	Receiver operating characteristic (ROC) curve and performance metrics for binary and multiclass classifiers (desde R2022b)

S

`sae`	Sum absolute error performance function
`satlin`	Función de transferencia lineal saturada
`satlins`	Función de transferencia lineal simétrica saturada
`scalprod`	Función de peso de producto de escalar
`scores2label`	Convert prediction scores to labels (desde R2024a)
`segmentCells2D`	Segment 2-D image using Cellpose (desde R2023b)
`segmentCells3D`	Segment 3-D image volume using Cellpose (desde R2023b)
`selfAttentionLayer`	Self-attention layer (desde R2023a)
`selforgmap`	Mapa autoorganizado
`separateSpeakers`	Separate signal by speakers (desde R2023b)
`separatewb`	Separar valores de sesgos y pesos de vectores de pesos/sesgos
`seq2con`	Convert sequential vectors to concurrent vectors
`sequenceFoldingLayer`	(Not recommended) Sequence folding layer
`sequenceInputLayer`	Capa de entrada de secuencias
`sequenceUnfoldingLayer`	(Not recommended) Sequence unfolding layer
`SeriesNetwork`	(No recomendado) Red en serie de deep learning
`setelements`	Set neural network data elements
`setL2Factor`	Set L₂ regularization factor of layer learnable parameter
`setLearnRateFactor`	Set learn rate factor of layer learnable parameter
`setsamples`	Set neural network data samples
`setsignals`	Set neural network data signals
`setsiminit`	Set neural network Simulink block initial conditions
`settimesteps`	Set neural network data timesteps
`setwb`	Set all network weight and bias values with single vector
`sgdmupdate`	Update parameters using stochastic gradient descent with momentum (SGDM) (desde R2019b)
`show`	Show training information plot (desde R2023b)
`shuffle`	Shuffle data in `augmentedImageDatastore`
`shuffle`	Shuffle data in minibatchqueue (desde R2020b)
`shufflenet`	(No recomendado) Red neuronal convolucional ShuffleNet preentrenada
`sigmoid`	Aplicar la activación sigmoide (desde R2019b)
`sigmoidLayer`	Capa sigmoide (desde R2020b)
`signalDatastore`	Datastore for collection of signals (desde R2020a)
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction (desde R2021b)
`signalLabelDefinition`	Create signal label definition
`signalMask`	Modify and convert signal masks and extract signal regions of interest (desde R2020b)
`signalTimeFeatureExtractor`	Streamline signal time feature extraction (desde R2021a)
`sigrangebinmask`	Label signal samples with values within a specified range (desde R2023a)
`sim`	Simular una red neuronal
`sim2nndata`	Convert Simulink time series to neural network data
`sinusoidalPositionEncodingLayer`	Sinusoidal position encoding layer (desde R2023b)
`softmax`	Apply softmax activation to channel dimension (desde R2019b)
`softmax`	Función de transferencia softmax
`softmaxLayer`	Capa softmax
`solov2`	Segment objects using SOLOv2 instance segmentation network (desde R2023b)
`sortClasses`	Sort classes of confusion matrix chart
`spatialDropoutLayer`	Spatial dropout layer (desde R2024a)
`splitlabels`	Find indices to split labels according to specified proportions (desde R2021a)
`squeezenet`	(No recomendado) Red neuronal convolucional SqueezeNet
`squeezesegv2Layers`	(Not recommended) Create SqueezeSegV2 segmentation network for organized lidar point cloud (desde R2020b)
`srchbac`	1-D minimization using backtracking
`srchbre`	1-D interval location using Brent’s method
`srchcha`	1-D minimization using Charalambous' method
`srchgol`	1-D minimization using golden section search
`srchhyb`	1-D minimization using a hybrid bisection-cubic search
`ssdObjectDetector`	Detect objects using SSD deep learning detector (desde R2020a)
`sse`	Función de rendimiento con suma de errores cuadráticos
`stack`	Stack encoders from several autoencoders together
`staticderiv`	Static derivative function
`stft`	Transformada de Fourier de tiempo corto
`stftLayer`	Short-time Fourier transform layer (desde R2021b)
`stftmag2sig`	Signal reconstruction from STFT magnitude (desde R2020b)
`stripdims`	Remove `dlarray` data format (desde R2019b)
`sumabs`	Suma de los elementos absolutos de una matriz o matrices
`summary`	Imprimir un resumen de la red (desde R2022b)
`sumsqr`	Suma de los cuadrados de los elementos de una matriz o matrices
`swishLayer`	Swish layer (desde R2021a)

T

`tanhLayer`	Capa de tangente hiperbólica (tanh)
`tansig`	Función de transferencia sigmoide tangente hiperbólica
`tapdelay`	Shift neural network time series data for tap delay
`taylorPrunableNetwork`	Network that can be pruned by using first-order Taylor approximation (desde R2022a)
`TFLiteModel`	TensorFlow Lite model (desde R2022a)
`timedelaynet`	Red neuronal de retardo de tiempo
`tonndata`	Convertir los datos al formato de arreglo de celdas de una red neuronal estándar
`train`	Entrenar una red neuronal superficial
`trainAutoencoder`	Entrenar un codificador automático
`trainb`	Batch training with weight and bias learning rules
`trainbfg`	BFGS quasi-Newton backpropagation
`trainbr`	Retropropagación de regularización bayesiana
`trainbu`	Batch unsupervised weight/bias training
`trainc`	Cyclical order weight/bias training
`traincgb`	Conjugate gradient backpropagation with Powell-Beale restarts
`traincgf`	Conjugate gradient backpropagation with Fletcher-Reeves updates
`traincgp`	Conjugate gradient backpropagation with Polak-Ribiére updates
`traingd`	Retropropagación del gradiente descendente
`traingda`	Gradient descent with adaptive learning rate backpropagation
`traingdm`	Retropropagación del gradiente descendente con momento
`traingdx`	Gradiente descendente con momento (inercia) y retropropagación de la tasa de aprendizaje adaptativo
`TrainingInfo`	Neural network training information (desde R2023b)
`trainingOptions`	Opciones para entrenar una red neuronal de deep learning
`TrainingOptionsADAM`	Training options for Adam optimizer
`TrainingOptionsLBFGS`	Training options for limited-memory BFGS (L-BFGS) optimizer (desde R2023b)
`TrainingOptionsRMSProp`	Training options for RMSProp optimizer
`TrainingOptionsSGDM`	Training options for stochastic gradient descent with momentum
`trainingProgressMonitor`	Monitor and plot training progress for deep learning custom training loops (desde R2022b)
`trainlm`	Retropropagación Levenberg-Marquardt
`trainnet`	Train deep learning neural network (desde R2023b)
`trainNetwork`	(No recomendado) Entrenar una red neuronal
`trainoss`	One-step secant backpropagation
`trainPointPillarsObjectDetector`	Train PointPillars object detector (desde R2021b)
`trainr`	Random order incremental training with learning functions
`trainrp`	Resilient backpropagation
`trainru`	Unsupervised random order weight/bias training
`trains`	Sequential order incremental training with learning functions
`trainscg`	Retropropagación de gradiente conjugado escalado
`trainSoftmaxLayer`	Train a softmax layer for classification
`trainWordEmbedding`	Train word embedding
`transform`	Transform datastore
`TransformedDatastore`	Datastore to transform underlying datastore
`transposedConv1dLayer`	Transposed 1-D convolution layer (desde R2022a)
`transposedConv2dLayer`	Transposed 2-D convolution layer
`transposedConv3dLayer`	Transposed 3-D convolution layer
`TransposedConvolution1DLayer`	Transposed 1-D convolution layer (desde R2022a)
`TransposedConvolution2DLayer`	Transposed 2-D convolution layer
`TransposedConvolution3DLayer`	Transposed 3-D convolution layer
`tribas`	Función de transferencia de base triangular
`trimdata`	Trim data by removing elements (desde R2023b)
`tritop`	Triangle layer topology function

U

`unconfigure`	Unconfigure network inputs and outputs
`unet`	Create U-Net convolutional neural network for semantic segmentation (desde R2024a)
`unet3d`	Create 3-D U-Net convolutional neural network for semantic segmentation of volumetric images (desde R2024a)
`unfreezeParameters`	Convert nonlearnable network parameters in `ONNXParameters` to learnable (desde R2020b)
`unpackProjectedLayers`	Unpack projected layers of neural network (desde R2023b)
`updateInfo`	Update information columns in experiment results table (desde R2021a)
`updateInfo`	Update information values for custom training loops (desde R2022b)
`updatePrunables`	Remove filters from prunable layers based on importance scores (desde R2022a)
`updateScore`	Compute and accumulate Taylor-based importance scores for pruning (desde R2022a)

V

`validate`	Quantize and validate a deep neural network (desde R2020a)
`vec2ind`	Convertir vectores en índices
`vec2word`	Map embedding vector to word
`verifyNetworkRobustness`	Verify adversarial robustness of deep learning network (desde R2022b)
`vgg16`	(No recomendado) Red neuronal convolucional VGG-16
`vgg19`	(No recomendado) Red neuronal convolucional VGG-19
`vggishEmbeddings`	Extract VGGish feature embeddings (desde R2022a)
`view`	Visualizar una red neuronal superficial
`view`	View autoencoder
`visionTransformer`	Pretrained vision transformer (ViT) neural network (desde R2023b)

W

`waveletScattering`	Wavelet time scattering
`word2ind`	Map word to encoding index
`word2vec`	Map word to embedding vector
`wordEmbedding`	Word embedding model to map words to vectors and back
`wordEmbeddingLayer`	Word embedding layer for deep learning neural network
`wordEncoding`	Word encoding model to map words to indices and back
`writeWordEmbedding`	Write word embedding file

X

xception (No recomendado) Red neuronal convolucional Xception

Y

`yolov2ObjectDetector`	Detect objects using YOLO v2 object detector
`yolov3ObjectDetector`	Detect objects using YOLO v3 object detector (desde R2021a)
`yolov4ObjectDetector`	Detect objects using YOLO v4 object detector (desde R2022a)
`yoloxObjectDetector`	Detect objects using YOLOX object detector (desde R2023b)
`yscale`	Set training plot y-axis scale (linear or logarithmic) (desde R2024a)