Overview
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- Classifying Images with Convolutional Networks: Get an overview of the course. Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.
- Interpreting Network Behavior: 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.
- Creating Networks: Build convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work.
- Training Networks: Understand how training algorithms work. Set training options to monitor and control training.
- Improving Performance: Choose and implement modifications to training algorithm options, network architecture, or training data to improve network performance.
- Spectrogram Classification Project:
- Performing Regression: Create convolutional networks that can predict continuous numeric responses.
- Using Deep Learning for Computer Vision: Train networks to locate and label specific objects within images.
- Classifying Sequence Data with Recurrent Networks: Build and train networks to perform classification on ordered sequences of data, such as time series or sensor data.
- Classifying Categorical Sequences: Use recurrent networks to classify sequences of categorical data, such as text.
- Generating Sequences of Output: Use recurrent networks to create sequences of predictions.
- Sequence Classification Project:
- Conclusion: Learn next steps and give feedback on the course.
