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.
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
App Training
- Train Networks Using Deep Network Designer
Interactively train deep learning networks in Deep Network Designer. - Import Data into Deep Network Designer
Import and visualize data in Deep Network Designer.
Command-Line Training
- Create Simple Deep Learning Neural Network for Classification
This example shows how to create and train a simple convolutional neural network for deep learning classification. - Train Convolutional Neural Network for Regression
This example shows how to fit a regression model using convolutional neural networks to predict the angles of rotation of handwritten digits. - Set Up Parameters and Train Convolutional Neural Network
Learn how to set up training parameters for a convolutional neural network. - 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.