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 Classification Using 1-D Convolutions
  This example shows how to classify sequence data using a 1-D convolutional neural 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.
• 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.
• Time Series Forecasting Using Deep Learning
  This example shows how to forecast time series data using a long short-term memory (LSTM) network.
• Time Series Anomaly Detection Using Deep Learning
  This example shows how to detect anomalies in sequence or time series data.
• 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.
• Speech Command Recognition Using Deep Learning
  This example shows how to train a deep learning model that detects the presence of speech commands in audio.
• 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.
• Visualize Activations of LSTM Network
  This example shows how to investigate and visualize the features learned by LSTM networks by extracting the activations.
• Sequence Classification Using Inverse-Frequency Class Weights
  This example shows how to classify sequences with a 1-D convolutional neural network using class weights that are inversely proportional to the frequency of the respective 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).
• Chemical Process Fault Detection Using Deep Learning
  Use simulation data to train a neural network than can detect faults in a chemical process.
• Build Networks with Deep Network Designer
  Interactively build and edit deep learning networks in Deep Network Designer.
• 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.
• Predict and Update Network State in Simulink
  This example shows how to predict responses for a trained recurrent neural network in Simulink® by using the Stateful 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 the Stateful 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.
• 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.