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Back Propagation
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Classroom Contents
A Practical Guide to Deep Learning - Richard Zemel
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- 1 Intro
- 2 Perceptrons
- 3 Brain inspiration
- 4 Layers
- 5 Classification
- 6 Gradient Descent
- 7 Back Propagation
- 8 Object Recognition
- 9 Vision and Vision
- 10 Neural networks
- 11 Local receptive fields
- 12 Somatosensory strip
- 13 Stride
- 14 Receptive fields
- 15 Feature detector
- 16 Weight sharing
- 17 Kernels
- 18 Edge detection
- 19 Threedimensional networks
- 20 Summary
- 21 pooling
- 22 upli
- 23 pooling layer
- 24 rotation layer
- 25 Natron
- 26 Regularizers
