Overview
Syllabus
Intro
Trickier cases
ConvNets match pieces of the image
Filtering: The math behind the match
Convolution: Trying every possible match
Pooling
Rectified Linear Units (ReLUS)
Fully connected layer
Input vector
A neuron
Squash the result
Weighted sum-and-squash neuron
Receptive fields get more complex
Add an output layer
Exhaustive search
Gradient descent with curvature
Tea drinking temperature
Chaining
Backpropagation challenge: weights
Backpropagation challenge: sums
Backpropagation challenge: sigmoid
Backpropagation challenge: ReLU
Training from scratch
Customer data
Taught by
Brandon Rohrer