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How Convolutional Neural Networks Work, in Depth
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- 1 Intro
- 2 Trickier cases
- 3 ConvNets match pieces of the image
- 4 Filtering: The math behind the match
- 5 Convolution: Trying every possible match
- 6 Pooling
- 7 Rectified Linear Units (ReLUS)
- 8 Fully connected layer
- 9 Input vector
- 10 A neuron
- 11 Squash the result
- 12 Weighted sum-and-squash neuron
- 13 Receptive fields get more complex
- 14 Add an output layer
- 15 Exhaustive search
- 16 Gradient descent with curvature
- 17 Tea drinking temperature
- 18 Chaining
- 19 Backpropagation challenge: weights
- 20 Backpropagation challenge: sums
- 21 Backpropagation challenge: sigmoid
- 22 Backpropagation challenge: ReLU
- 23 Training from scratch
- 24 Customer data
