📗 The quizzes must be completed during the lectures and submitted on TopHat: Link. No Canvas submissions are required. The grades will be updated by the end of the week on Canvas.
📗 Please submit a regrade request if (i) you missed a few questions because you are late or have to leave during the lecture; (ii) you selected obviously incorrect answers by mistake (one or two of these shouldn't affect your grade): Link
The following questions may appear as quiz questions during the lecture. If the questions are not generated correctly, try refresh the page using the button at the top left corner.
📗 [4 points] Which one of the following LTUs (Linear Threshold Unit) represent the following binary operators?
\(x_{1}\)
\(x_{2}\)
(A)
(B)
(C)
(D)
(E)
0
0
0
1
1
0
1
1
1.
2.
3.
4.
5.
📗 For example, if you think 1 is matched with A, D, 2 is matched with B and C, 4 is matched with E ..., enter (1, 2, 2, 1, 4).
📗 Answer (comma separated vector): .
📗 [4 points] Given the following neural network that classifies all the training instances correctly. What are the labels (0 or 1) of the training data? The activation functions are LTU for all units: \(1_{\left\{z \geq 0\right\}}\). The first layer weight matrix is , with bias vector , and the second layer weight vector is , with bias
\(x_{i1}\)
\(x_{i2}\)
\(y_{i}\) or \(a^{\left(2\right)}_{1}\)
0
0
?
0
1
?
1
0
?
1
1
?
Note: if the weights are not shown clearly, you could move the nodes around with mouse or touch.
📗 Answer (comma separated vector): .
📗 [2 points] In a three-layer (fully connected) neural network, the first hidden layer contains sigmoid units, the second hidden layer contains units, and the output layer contains units. The input is dimensional. How many weights plus biases does this neural network have? Enter one number.
📗 The above is a diagram of the network, the nodes labelled "1" are the bias units.