Completed
Intro
Class Central Classrooms beta
YouTube playlists curated by Class Central.
Classroom Contents
How Convolutional Neural Networks Work, in Depth
Automatically move to the next video in the Classroom when playback concludes
- 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
