CS 451 Quiz 10
Neural network implementation and application
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Where does the concept of "symmetry breaking" appear in neural network learning? *
Suppose you train two identical neural nets using the same training set. Each net has 4 layers with 10 nodes each. The only difference is that network 1 starts with theta initialized to all zeros and network 2 starts with a random theta. What will happen to the accuracy after, say, 1000 iterations? *
What's a good value for epsilon in gradient checking? *
Should gradient checking be turned off before training your classifier? *
If you have two Theta matrices of size 10x11 and 2x11, unrolling parameters yields *
In the autonomous driving example shown in the video, a neural net is given input images and learns to *
Unlike for logistic regression, the cost function J for multi-layer neural nets is non-convex and thus gradient descent can end up in a local minimum *
When deciding on a network architecture, (check all that apply) *
You compute (J(x+e) - J(x-e))/(2*e) if you want to do *
Which Octave operation unrolls a 5x5 matrix M into a vector v? *
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