After defining the network architecture, you can define training
parameters using the
trainingOptions function. You
can then train the network using
trainnet. Use the trained network to predict class labels or
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
|Deep Network Designer||Design, visualize, and train deep learning networks|
|Classify data using trained deep learning neural network|
|Predict responses using trained deep learning neural network|
|Compute deep learning network layer activations|
|Create confusion matrix chart for classification problem|
|Sort classes of confusion matrix chart|
- 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.