Office Use Only
Semester One 2019
Examination Period
Faculty of Information Technology
EXAM CODES: FIT3181
TITLE OF PAPER: Final Examination Paper for Unit FIT3181 Deep Learning
EXAM DURATION: 2 hours writing time
READING TIME: 10 minutes
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FIT3181 Deep Learning Final Exam Paper, T1, 2019 | Page 1 of 9
PART A: Multiple-Choice Questions
● This part contains 8 multiple-choice questions
● The total number of marks for this part is 15
● For multiple-choice question, you must select all applicable answers to receive the full
mark for that question.
Question A1 [1 mark]
In a CNN, unlike the convolutional layer, the pooling layer has no learnable parameters
a) True
b) False
Solution: (a)
Question A2 [3 marks]
Consider a machine learning problem to detect breast cancer from a dataset consisting of
mammograms and medical data collected from a cohort of patients. The task is to predict whether
a patient has breast cancer. The following table summarizes the confusion matrix on the test
dataset. Select all applicable answers below:
True Labels
Pre
dic
ted
Cla
ss
CANCER (1) NORMAL (0)
CANCER (1) 9 10
NORMAL (0) 1 90
a) The total number of instances is 110 with 10 labelled as CANCER and 100 labelled as
NORMAL
b) The true positive rate (TPR) is 9/10 = 90%
c) This test dataset is a balanced dataset
d) It is not possible to calculate the AUC because we don’t have the ROC curve which requires
the performance information at different level of thresholds
Solution: (a), (b), (d)
Question A3 [2 marks]
When training a deep neural network (DNN), which of the following statements are applicable
a) One can use a Stochastic Gradient method known as the Back Propagation algorithm to
optimize the loss function
b) The Back Propagation method is robust against overfitting, hence likely to always produce a
good global optimal solution
c) The learning rate is an important parameter when training a DNN
d) With TensorFlow, a simple way to detect the problem of gradient vanishing is to draw the
histograms of the gradients and visually inspect them in TensorBoard
Solution: (a), (c), (d)
FIT3181 Deep Learning Final Exam Paper, T1, 2019 | Page 2 of 9
Question A4 [2 marks]
Factors which have driven recent success in deep learning include:
a) Deep learning models are generally very flexible and powerful
b) Modern advancements in hardware have enabled fast distributed and parallel computation
c) The recent release of iPhone X has enabled centralized computation on a single device
d) The availability of massive scale modern datasets has enabled computer scientists to train
powerful machine learning models
Solution: (a), (b), (d)
Question A5 [2 marks]
Which of the following statement are true regarding the Gradient Descent (GD) method when
applied to minimize the objective function J(w):
a) With appropriate learning rate, GD guarantees to converge to a global minimum if J(w) is a
convex function
b) With appropriate learning rate, GD always guarantees to converge to some local minimum
if J(w) is a nonconvex function
c) GD updates the parameter in the opposite direction of the current gradient
d) GD is a second-order optimization method
e) GD runs much faster than Stochastic GD for large datasets
Solution: (a), (c)
Question A6 [2 marks]
With respect to the Pooling layer in a CNN, which of the following statements are true
a) It operates at the combination of multiple activation maps to produce a dependent output
b) It reduces the resolution of the image, hence provides a better computational efficiency
c) Max-pooling is locally invariant in the sense that input numbers within a local filter window
can be shuffled without changing the final output
d) Output tensor of a max-pooling layer will always have the same depth with the input tensor
Solution: (b), (c), (d)
Question A7 [1 mark]
Consider a text modelling problem where a corpus of texts is given and contains two words ‘king’
and ‘queen’. If these two words are to be represented as one-hot encoding vector, what is the
possible value computed for their cosine similarity
a) 0
b) 0.25
c) 0.5
d) 1.0
e) None of above
Solution: (a)
Question A8 [2 marks]
FIT3181 Deep Learning Final Exam Paper, T1, 2019 | Page 3 of 9
You were given a corpus of texts which is assumed to be sufficiently large to learn semantic
meaning of words. After applying word2vec embedding on this corpus, each word had been
associated with a vector. As you have learned from the lecture, this now allows you to perform
analogical reasoning. Which answer do you expect if we reason “mom – dad = – man”
a) child
b) girl
c) mother
d) woman
Solution: (d)
PART B: Short Workout Questions
● This part contains 6 workout questions
● The total number of marks for this part is 35
Question B1 [3 marks]
Draw the computational graph for the function , , ( ) = (2 + 2 + )
Solution:
Question B2 [5 marks]
Generative Adversarial Networks (GAN) is a deep generative model that uses a generator
to simulate data by mapping a random through . Given a training dataset
θ
( ) ~
