- Importing image and sequence data
- Using convolutional neural networks for image classification, regression, and other image applications
- Using long short-term memory networks for sequence classification and forecasting
- Modifying common network architectures to solve custom problems
- Improving the performance of a network by modifying training options
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Day 1 of 2
Transfer Learning for Image Classification
Objective: Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.
- Pretrained networks
- Image datastores
- Transfer learning
- Network evaluation
Interpreting Network Behavior
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.
- 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
Day 2 of 2
Training a Network and Improving Performance
Objective: Understand how training algorithms work. Set training options to monitor and control training. Choose and implement modifications to training algorithm options, network architecture, or training data to improve network performance.
- Network training
- Training progress plots
- Training options
- Directed acyclic graphs
- Augmented datastores
Performing Image Regression
Objective: Create convolutional networks that can predict continuous numeric responses.
- Transfer learning for regression
- Evaluation metrics for regression networks
Using Deep Learning for Computer Vision
Objective: Train networks to locate and label specific objects within images.
- Image application workflow
- Object detection
Sequence Data Classification and Generation
Objective: Build and train networks to perform classification on ordered sequences of data, such as time series or sensor data. Use recurrent networks to create sequences of predictions.
- Long short-term memory networks
- Sequence classification
- Sequence preprocessing
- Categorical sequences
- Sequence to sequence classification
- Sequence forecasting