Built-In Training
After defining the network architecture, you can define training
parameters using the trainingOptions
function. You
can then train the network using trainNetwork
or trainnet
. Use the trained network to predict class labels or
numeric responses, or forecast future time steps.
You can train a neural network on a CPU, a GPU, multiple
CPUs or GPUs, or in parallel on a cluster or in the cloud. Training on a GPU
or in parallel requires Parallel Computing Toolbox™. Using a GPU requires a supported GPU device (for information
on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox)).
Specify the execution environment using the trainingOptions
function.
Apps
Deep Network Designer | Design, visualize, and train deep learning networks |
Functions
Topics
Multilayer Perceptron Networks
- Train Network with Numeric Features
This example shows how to create and train a simple neural network for deep learning feature data classification. - Compare Deep Learning Networks for Credit Default Prediction
Create, train, and compare three deep learning networks for predicting credit default probability. - Battery State of Charge Workflow
An example workflow for training, compressing, and using a deep learning network in Simulink®.
Recurrent Networks
- 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. - 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. - Train Network with LSTM Projected Layer
Train a deep learning network with an LSTM projected layer for sequence-to-label classification. - 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. - 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.
Convolutional Networks
- 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 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. - 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 on Image and Feature Data
This example shows how to train a network that classifies handwritten digits using both image and feature input data.
Deep Learning with 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. - Data Sets for Deep Learning
Discover data sets for various deep learning tasks.