Monitor deep learning training progress using built-in or custom plots, and use the plots and results to assess model accuracy and loss. To improve network performance, you can tune training options or use Bayesian optimization to search for optimal hyperparameters. To explore results, you can visualize features learned by a network and create deep dream visualizations. Test your trained network by making predictions using new data.
This example shows how to classify images from a webcam in real time using the pretrained deep convolutional neural network AlexNet.
Learn how to set up training parameters for a convolutional neural network
When you train networks for deep learning, it is often useful to monitor the training progress.
This example shows how to feed an image to a convolutional neural network and display the activations of different layers of the network.
This example shows how to visualize the features learned by convolutional neural networks.
This example shows how to define an output function that runs at each iteration during training of deep learning neural networks.
Learn how to save checkpoint networks while training a convolutional neural network and resume training from a previously saved network
This example shows how to apply Bayesian optimization to deep learning and find optimal network parameters and training options for convolutional neural networks.