Data Preprocessing

Manage and preprocess sequence and tabular data for deep learning

Preprocessing data is a common first step in the deep learning workflow to prepare raw data in a format that the network can accept. For example, you can preprocess data to enhance desired features or reduce artifacts that can bias the network. For example, you can normalize or remove noise from input data.

You can preprocess sequence input with operations such as normalization by using datastores and functions available in MATLAB^® and Deep Learning Toolbox™. Other MATLAB toolboxes offer functions, datastores, and apps for labeling, processing, and augmenting deep learning data. Use specialized tools from other MATLAB toolboxes to process data for domains such as audio, text, and signal processing.

Apps

Video Labeler	Label video for computer vision applications
Ground Truth Labeler	Label ground truth data for automated driving applications
Signal Labeler	Label signal attributes, regions, and points of interest, and extract features

Functions

`transform`	Transform datastore
`combine`	Combine data from multiple datastores
`TransformedDatastore`	Datastore to transform underlying datastore
`CombinedDatastore`	Datastore to combine data read from multiple underlying datastores
`padsequences`	Pad or truncate sequence data to same length (Since R2021a)
`minibatchqueue`	Create mini-batches for deep learning (Since R2020b)

Topics

Sequence Classification Using Deep Learning
This example shows how to classify sequence data using a long short-term memory (LSTM) network.
Train Network with Numeric Features
This example shows how to create and train a simple neural network for deep learning feature data classification.
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.
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.
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.
Datastores for Deep Learning
Learn how to use datastores in deep learning applications.
Optimize Datastores for Deep Learning Performance
Explore methods for speeding up deep learning workflows that use datastores.
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.

Featured Examples

Time Series Forecasting Using Deep Learning

Forecast time series data using a long short-term memory (LSTM) network.

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Train Speech Command Recognition Model Using Deep Learning

Train a deep learning model that detects the presence of speech commands in audio. The example uses the Speech Commands Dataset [1] to train a convolutional neural network to recognize a set of commands.

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Chemical Process Fault Detection Using Deep Learning

Use simulation data to train a neural network than can detect faults in a chemical process.

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