Deep learning con series de tiempo y datos secuenciales
Cree y entrene redes para tareas de clasificación, regresión y predicción de series de tiempo
Cree y entrene redes para tareas de clasificación, regresión y predicción de series de tiempo. Entrene redes de memoria de corto-largo plazo (LSTM) para problemas de clasificación y regresión de secuencia a uno o secuencia a etiqueta. Puede entrenar redes LSTM sobre datos de texto usando capas de incrustación de palabras (requiere Text Analytics Toolbox™) o redes neuronales convolucionales sobre datos de audio mediante espectrogramas (requiere Audio Toolbox™).
Apps
Deep Network Designer | Diseñar, visualizar y entrenar redes de deep learning |
Funciones
Bloques
Propiedades
ConfusionMatrixChart Properties | Confusion matrix chart appearance and behavior |
Temas
Redes recurrentes
- Redes de memoria de corto-largo plazo
Obtenga información sobre redes de memoria de corto-largo plazo (LSTM). - Pronóstico de series de tiempo mediante deep learning
Este ejemplo muestra cómo pronosticar datos de series de tiempo mediante una red de memoria de corto-largo plazo (LSTM). - Clasificación de secuencias mediante deep learning
Este ejemplo muestra cómo clasificar datos secuenciales mediante una red de memoria de corto-largo plazo (LSTM). - Clasificación secuencia a secuencia mediante deep learning
Este ejemplo muestra cómo clasificar cada unidad de tiempo de datos secuenciales mediante una red de memoria de corto-largo plazo (LSTM). - Regresión de secuencia a secuencia mediante deep learning
Este ejemplo muestra cómo predecir la vida útil restante (RUL) de motores mediante deep learning. - Sequence-to-One Regression Using Deep Learning
This example shows how to predict the frequency of a waveform using a long short-term memory (LSTM) neural network. - Train Network with LSTM Projected Layer
Train a deep learning network with an LSTM projected layer for sequence-to-label classification. - Create Simple Sequence Classification Network Using Deep Network Designer
This example shows how to create a simple long short-term memory (LSTM) classification network using Deep Network Designer. - Classify Videos Using Deep Learning
This example shows how to create a network for video classification by combining a pretrained image classification model and an LSTM network. - Classify Videos Using Deep Learning with Custom Training Loop
This example shows how to create a network for video classification by combining a pretrained image classification model and a sequence classification network. - Image Captioning Using Attention
This example shows how to train a deep learning model for image captioning using attention. - Train Network Using Custom Mini-Batch Datastore for Sequence Data
This example shows how to train a deep learning network on out-of-memory sequence data using a custom mini-batch datastore. - Visualizar activaciones de redes de LSTM
Este ejemplo muestra cómo investigar y visualizar las características aprendidas por las redes de LSTM extrayendo las activaciones. - Chemical Process Fault Detection Using Deep Learning
Use simulation data to train a neural network than can detect faults in a chemical process. - Create Simple Sequence Classification Network Using Deep Network Designer
This example shows how to create a simple long short-term memory (LSTM) classification network using Deep Network Designer. - Train Latent ODE Network with Irregularly Sampled Time-Series Data
This example shows how to train a latent ordinary differential equation (ODE) autoencoder with time-series data that is sampled at irregular time intervals.
Redes convolucionales
- Sequence Classification Using 1-D Convolutions
This example shows how to classify sequence data using a 1-D convolutional neural network. - Time Series Anomaly Detection Using Deep Learning
This example shows how to detect anomalies in sequence or time series data. - Train Speech Command Recognition Model Using Deep Learning
This example shows how to train a deep learning model that detects the presence of speech commands in audio. - Train Sequence Classification Network Using Data With Imbalanced Classes
This example shows how to classify sequences with a 1-D convolutional neural network using class weights to modify the training to account for imbalanced classes. - Sequence-to-Sequence Classification Using 1-D Convolutions
This example shows how to classify each time step of sequence data using a generic temporal convolutional network (TCN). - Train Network with Complex-Valued Data
This example shows how to predict the frequency of a complex-valued waveform using a 1-D convolutional neural network. - Interpret Deep Learning Time-Series Classifications Using Grad-CAM
This example shows how to use the gradient-weighted class activation mapping (Grad-CAM) technique to understand the classification decisions of a 1-D convolutional neural network trained on time-series data. - Sequence Classification Using CNN-LSTM Network
This example shows how to create a 2-D CNN-LSTM network for speech classification tasks by combining a 2-D convolutional neural network (CNN) with a long short-term memory (LSTM) layer. - Train Network with Numeric Features
This example shows how to create and train a simple neural network for deep learning feature data classification.
Deep learning con Simulink
- Predict and Update Network State in Simulink
This example shows how to predict responses for a trained recurrent neural network in Simulink® by using theStateful Predict
block. - Classify and Update Network State in Simulink
This example shows how to classify data for a trained recurrent neural network in Simulink® by using theStateful Classify
block. - Predict Battery State of Charge Using Deep Learning
This example shows how to train a neural network to predict the state of charge of a battery by using deep learning. - Improve Performance of Deep Learning Simulations in Simulink
This example shows how to use code generation to improve the performance of deep learning simulations in Simulink®. - Physical System Modeling Using LSTM Network in Simulink
This example shows how to create a reduced order model (ROM) to replace a Simscape component in a Simulink® model by training a long short-term memory (LSTM) neural network.
Deep learning con MATLAB
- List of Deep Learning Layers
Discover all the deep learning layers in MATLAB®. - Datastores for Deep Learning
Learn how to use datastores in deep learning applications. - Deep Learning in MATLAB
Discover deep learning capabilities in MATLAB using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds. - Deep Learning Tips and Tricks
Learn how to improve the accuracy of deep learning networks. - Data Sets for Deep Learning
Discover data sets for various deep learning tasks.