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 Toolbox™).

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**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.

**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.

**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 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.

**Visualize Activations of LSTM Network**

This example shows how to investigate and visualize the features learned by LSTM networks by extracting the activations.

**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).

**Chemical Process Fault Detection Using Deep Learning**

This example shows how to use simulation data to train a neural network that can detect faults in a chemical process.

**Build Networks with Deep Network Designer**

Interactively build and edit deep learning networks.

**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.

**Classify Text Data Using Convolutional Neural Network**

This example shows how to classify text data using a convolutional neural network.

**Classify Out-of-Memory Text Data Using Deep Learning**

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

**Sequence-to-Sequence Translation Using Attention**

This example shows how to convert decimal strings to Roman numerals using a recurrent sequence-to-sequence encoder-decoder model with attention.

**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.

**Long Short-Term Memory Networks**

Learn about long short-term memory (LSTM) networks

Discover all the deep learning layers in MATLAB^{®}.

Learn how to use datastores in deep learning applications.

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.

Learn how to improve the accuracy of deep learning networks.