Objective: Get an overview of the course. Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.

• Pretrained networks
• Image datastores
• Transfer learning
• Network evaluation

Objective: Gain insight into how a network is operating by visualizing image data as it passes through the network. Apply this technique to different kinds of images.

• Activations
• Images from signal data
• Feature extraction for machine learning

Objective: Build convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work.

• Training from scratch
• Neural networks
• Convolution layers and filters

Objective: Understand how training algorithms work. Set training options to monitor and control training.

• Network training
• Training progress plots
• Validation

Objective: Choose and implement modifications to training algorithm options, network architecture, or training data to improve network performance.

• Training options
• Augmented datastores
• Directed acyclic graphs

Objective: Create convolutional networks that can predict continuous numeric responses

• Transfer learning for regression
• Evaluation metrics for regression networks

Objective: Train networks to locate and label specific objects within images.

• Object detection

Objective: Build and train networks to perform classification on ordered sequences of data, such as time series or sensor data.

• Long short-term memory networks
• Sequence classification
• Sequence preprocessing

Objective: Use recurrent networks to classify sequences of categorical data, such as text.

• Categorical sequences
• Text classification

Objective: Use recurrent networks to create sequences of predictions.

• Sequence to sequence classification
• Sequence forecasting