Tuning

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

This example shows how to save checkpoint networks while training a deep learning 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.

Train Network Using Custom Training Loop

This example shows how to train a network that classifies handwritten digits with a custom learning rate schedule.

Deep Learning Tips and Tricks

Learn how to improve the accuracy of deep learning networks.

Experimentation

Create a Deep Learning Experiment for Classification

This example shows how to train a deep learning network for classification by using Experiment Manager.

Create a Deep Learning Experiment for Regression

This example shows how to train a deep learning network for regression by using Experiment Manager.

Evaluate Deep Learning Experiments by Using Metric Functions

This example shows how to use metric functions to evaluate the results of an experiment.

Try Multiple Pretrained Networks for Transfer Learning

This example shows how to configure an experiment that replaces layers of different pretrained networks for transfer learning.

Experiment with Weight Initializers for Transfer Learning

This example shows how to configure an experiment that initializes the weights of convolution and fully connected layers using different weight initializers for training.

Run Multiple Deep Learning Experiments in Parallel

This example shows how to run multiple deep learning experiments on your local machine.

Visualization

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

This example shows how to use the gradient-weighted class activation mapping (Grad-CAM) technique to understand why a deep learning network makes its classification decisions.

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 Classification Decisions Using Gradient Attribution Techniques

This example shows how to use gradient attribution maps to investigate which parts of an image are most important for classification decisions made by a deep neural network.

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.

Visualize Image Classifications Using Maximal and Minimal Activating Images

This example shows how to use a data set to find out what activates the channels of a deep neural network.

View Network Behavior Using tsne

This example shows how to use the tsne function to view activations in a trained network.

Monitor GAN Training Progress and Identify Common Failure Modes

Learn how to diagnose and fix some of the most common failure modes in GAN training.

Deep Dream Images Using GoogLeNet

This example shows how to generate images using deepDreamImage with the pretrained convolutional neural network GoogLeNet.

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.