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Built-In Training

Train deep learning networks for image data using built-in training functions

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.


Deep Network DesignerDesign, visualize, and train deep learning networks


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trainingOptionsOptions for training deep learning neural network
trainNetworkTrain neural network
trainnetTrain deep learning neural network (Since R2023b)
analyzeNetworkAnalyze deep learning network architecture
classifyClassify data using trained deep learning neural network
predictPredict responses using trained deep learning neural network
activationsCompute deep learning network layer activations
confusionchartCreate confusion matrix chart for classification problem
sortClassesSort classes of confusion matrix chart


App Training

Command-Line Training