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General approach
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Classroom Contents
Training Neural Networks for Computer Vision - Part I - Lecture 10
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- 1 Intro
- 2 UCF Network Parameters - recap
- 3 Convolution - Intuition
- 4 General CNN architecture - recap
- 5 Learning phases - recap Images
- 6 Network Training - Minimize Cost
- 7 General approach
- 8 Train CNN with Gradient Descent
- 9 Loss Functions
- 10 Differentiability
- 11 Backpropagation - Chain Rule
- 12 Optimization demo
- 13 Stochastic Gradient Descent
