Duration
21 hours (usually 3 days including breaks)
Requirements
Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).
Overview
This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.
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