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Deep Learning with Time Series, Sequences, and Text

Create and train networks for time series classification, regression, and forecasting tasks

Create and train networks for time series classification, regression, and forecasting tasks. Train long short-term memory (LSTM) networks for sequence-to-one or sequence-to-label classification and regression problems. You can train LSTM networks on text data using word embedding layers (requires Text Analytics Toolbox™) or convolutional neural networks on audio data using spectrograms (requires Audio System Toolbox™).

Apps

Deep Network DesignerEdit and build deep learning networks

Functions

expand all

trainingOptionsOptions for training deep learning neural network
trainNetworkTrain neural network for deep learning
analyzeNetworkAnalyze deep learning network architecture
sequenceInputLayerSequence input layer
lstmLayerLong short-term memory (LSTM) layer
bilstmLayerBidirectional long short-term memory (BiLSTM) layer
fullyConnectedLayerFully connected layer
reluLayerRectified Linear Unit (ReLU) layer
leakyReluLayerLeaky Rectified Linear Unit (ReLU) layer
clippedReluLayerClipped Rectified Linear Unit (ReLU) layer
dropoutLayerDropout layer
softmaxLayerSoftmax layer
classificationLayerClassification output layer
regressionLayerCreate a regression output layer
predictPredict responses using a trained deep learning neural network
classifyClassify data using a trained deep learning neural network
predictAndUpdateStatePredict responses using a trained recurrent neural network and update the network state
classifyAndUpdateStateClassify data using a trained recurrent neural network and update the network state
resetStateReset the state of a recurrent neural network
confusionchartCreate confusion matrix chart for classification problem
ConfusionMatrixChart PropertiesConfusion matrix chart appearance and behavior
sortClassesSort classes of confusion matrix chart

Examples and How To

Sequences and Time Series

Sequence Classification Using Deep Learning

This example shows how to classify sequence data using a long short-term memory (LSTM) network.

Sequence-to-Sequence Classification Using Deep Learning

This example shows how to classify each time step of sequence data using a long short-term memory (LSTM) network.

Sequence-to-Sequence Regression Using Deep Learning

This example shows how to predict the remaining useful life (RUL) of engines by using deep learning.

Time Series Forecasting Using Deep Learning

This example shows how to forecast time series data using a long short-term memory (LSTM) network.

Speech Command Recognition Using Deep Learning

This example shows how to train a simple deep learning model that detects the presence of speech commands in audio.

Train Network Using Out-of-Memory Sequence Data

This example shows how to train a deep learning network on out-of-memory sequence data using a custom mini-batch datastore.

Build Networks with Deep Network Designer

Interactively build and edit deep learning networks.

Text Data

Classify Text Data Using Deep Learning

This example shows how to classify text descriptions of weather reports using a deep learning long short-term memory (LSTM) network.

Generate Text Using Deep Learning

This example shows how to train a deep learning long short-term memory (LSTM) network to generate text.

Pride and Prejudice and MATLAB

This example shows how to train a deep learning LSTM network to generate text using character embeddings.

Word-By-Word Text Generation Using Deep Learning

This example shows how to train a deep learning LSTM network to generate text word-by-word.

Classify Out-of-Memory Text Data Using Custom Mini-Batch Datastore

This example shows how to classify out-of-memory text data with a deep learning network using a custom mini-batch datastore.

Concepts

Long Short-Term Memory Networks

Learn about long short-term memory (LSTM) networks

List of Deep Learning Layers

Discover all the deep learning layers in MATLAB®.

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.

Featured Examples