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Course Outline
- Backprop, modular models
- Logsum module
- RBF Net
- MAP/MLE loss
- Parameter Space Transforms
- Convolutional Module
- Gradient-Based Learning
- Energy for inference
- Objective for learning
- PCA, NLL
- Latent Variable Models
- Probabilistic LVM
- Loss Function
- Handwriting recognition
Requirements
Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).
21 Hours
Testimonials (1)
The structure from first principles, to case studies, to application.
Margaret Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to Deep Learning
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