Monitor deep learning training progress using built-in plots of network accuracy and loss. To improve network performance, you can tune training options and use Bayesian optimization to search for optimal hyperparameters. To investigate trained networks, you can visualize features learned by a network and create deep dream visualizations. Test your trained network by making predictions using new data.
Deep Network Designer | Edit and build deep learning networks |
Set Up Parameters and Train Convolutional Neural Network
Learn how to set up training parameters for a convolutional neural network
Resume Training from Checkpoint Network
Learn how to save checkpoint networks while training a convolutional neural network and resume training from a previously saved network
Deep Learning Using Bayesian Optimization
This example shows how to apply Bayesian optimization to deep learning and find optimal network hyperparameters and training options for convolutional neural networks.
Run Multiple Deep Learning Experiments
This example shows how to run multiple deep learning experiments on your local machine.
Train Network Using Custom Training Loop
This example shows how to train a network that classifies handwritten digits with a custom learning rate schedule.
Learn how to improve the accuracy of deep learning networks.
Classify Webcam Images Using Deep Learning
This example shows how to classify images from a webcam in real time using the pretrained deep convolutional neural network GoogLeNet.
Monitor Deep Learning Training Progress
When you train networks for deep learning, it is often useful to monitor the training progress.
Grad-CAM Reveals the Why Behind Deep Learning Decisions
Grad-CAM explains why a network makes a decision.
Understand Network Predictions Using Occlusion
This example shows how to use occlusion sensitivity maps to understand why a deep neural network makes a classification decision.
Investigate Network Predictions Using Class Activation Mapping
This example shows how to use class activation mapping (CAM) to investigate and explain the predictions of a deep convolutional neural network for image classification.
View Network Behavior Using tsne
This example shows how to use the tsne
function to view activations in a trained network.
Visualize Activations of a Convolutional Neural Network
This example shows how to feed an image to a convolutional neural network and display the activations of different layers of the network.
Visualize Activations of LSTM Network
This example shows how to investigate and visualize the features learned by LSTM networks by extracting the activations.
Visualize Features of a Convolutional Neural Network
This example shows how to visualize the features learned by convolutional neural networks.