WorksheetsATI FINAL PART 3
Total questions: 32
Worksheet time: 24mins
Name
Class
Date
1.
In logistic function, when we increase the value of „w‟, the function becomes
a)
Step function
b)
Sigmoid function
c)
quadratic function
d)
None of these
2.
Which parameter(s) need to be learnt in minimizing objective function in supervised learning
a)
Only Weight
b)
Only Bias
c)
Both Weight and Bias
d)
Learning rate
3.
Which of the following statement is true?
a)
An event with high probability has high information content
b)
An event with low probability has high information content
c)
An event with low probability has low information content
d)
None of the above
4.
Why convolution neural network is taking off quickly in recent times
a)
Access to large amount of digitized data
b)
Integration of feature extraction within the training process
c)
Availability of more computational power
d)
All of the above
5.
Momentum based gradient descent algorithm and Nesterov accelerated gradient descent are faster than Stochastic gradient descent algorithm”
a)
True
b)
False
6.
Consider the following statement, “It takes less time to navigate the regions having a gentle slope” The above statement is true in case of I. Gradient descent algorithm II. Momentum based gradient descent algorithm
a)
I
b)
II
c)
II &I
7.
. Identify the technique that is used to achieve relatively better learning rate by updating w using bunch of different values of η.
a)
Bias Correction
b)
Line Search
c)
Stochastic
d)
All the above
8.
There is no guarantee that the loss decreases at each step in a stochastic Gradient Descent”
a)
True
b)
False
9.
. Identify the advantages of Nesterov accelerated gradient descent. I. Corrects its course quicker than Momentum-based gradient descent II. Oscillations are smaller III. Chances of escaping minima valley are also smaller
a)
I
b)
only II
c)
II and III
d)
I,II, andIII
10.
6. Pick out the methods for annealing learning rate that has only number of epochs as the hyperparameter
a)
Step decay
b)
Exponential Decay
c)
1/t Decay
11.
Adagrad got stuck when it was close to convergence. How does RMSProp overcome this problem?
a)
More Aggressive on decay
b)
Less Aggressive on decay
c)
No decay
12.
In a neural network, knowing the weight and bias of each neuron is the most important step. If you can somehow get the correct value of weight and bias for each neuron, you can approximate any function. What would be the best way to approach this?
a)
Assign random values and pray to God they are correct
b)
Search every possible combination of weights and biases till you get the best value
c)
Iteratively check that after assigning a value how far you are from the best values, and slightly change the assigned values values to make them better
d)
None of these
13.
What are the steps for using a gradient descent algorithm? 1. Calculate error between the actual value and the predicted value 2. Reiterate until you find the best weights of network 3. Pass an input through the network and get values from output layer 4. Initialize random weight and bias 5. Go to each neurons which contributes to the error and change its respective values to reduce the error
a)
1, 2, 3, 4, 5
b)
5, 4, 3, 2, 1
c)
3, 2, 1, 5, 4
d)
4, 3, 1, 5, 2
14.
“Convolutional Neural Networks can perform various types of transformation (rotations or scaling) in an input”. Is the statement correct True or False?
a)
True
b)
False
15.
Which of the following techniques perform similar operations as dropout in a neural network?
a)
Bagging
b)
Boosting
c)
Stacking
d)
None of these
16.
Which of the following gives non-linearity to a neural network?
a)
) Stochastic Gradient Descent
b)
Rectified Linear Unit
c)
Convolution function
d)
None of the above
17.
Which of the following is true about model capacity (where model capacity means the ability of
neural network to approximate complex functions) ?
a)
As number of hidden layers increase, model capacity increases
b)
As dropout ratio increases, model capacity increases
c)
As learning rate increases, model capacity increases
d)
None of these
18.
If you increase the number of hidden layers in a Multi Layer Perceptron, the classification error of test data always decreases. True or False?
a)
True
b)
B) False
19.
Can a neural network model the function (y=1/x)?
a)
Yes
b)
No
20.
In which neural net architecture, does weight sharing occur?
a)
) Convolutional neural Network
b)
Recurrent Neural Network
c)
Fully Connected Neural Network
d)
d Neural Network (D) Both A and B
21.
Batch Normalization is helpful because
a)
It normalizes (changes) all the input before sending it to the next layer
b)
It returns back the normalized mean and standard deviation of weights
c)
t is a very efficient backpropagation technique
d)
None of these
22.
. Instead of trying to achieve absolute zero error, we set a metric called bayes error which is the error we hope to achieve. What could be the reason for using bayes error?
a)
Input variables may not contain complete information about the output variable
b)
System (that creates input-output mapping) may be stochastic
c)
Limited training data
23.
The number of neurons in the output layer should match the number of classes (Where the number of classes is greater than 2) in a supervised learning task. True or False?
a)
True
b)
False
24.
What is the purpose of Gradient Descent algorithm?
a)
. To normalize the inputs
b)
To minimize the weight and bias
c)
To minimize the loss function
d)
To prevent overfitting
25.
Which is true for neural networks?
a)
It has set of nodes and connections
b)
Each node computes it's weighted input
c)
Node could be in excited state or non-excited state
d)
. All of the mentioned
26.
What is a dead unit in a neural network?
a)
A unit which doesn’t update during training by any of its neighbour
b)
A unit which does not respond completely to any of the training patterns
c)
The unit which produces the biggest sum-squared error
d)
) None of these
27.
.Which of the following statement is the best description of early stopping?
a)
Train the network until a local minimum in the error function is reached
b)
Simulate the network on a test dataset after every epoch of training. Stop training when the generalization error starts to increase
c)
Add a momentum term to the weight update in the Generalized Delta Rule, so that raining converges more quickly
d)
A faster version of backpropagation, such as the `Quickprop‟ algorithm
28.
.What if we use a learning rate that’s too large?
a)
Network will converge
b)
Network will not converge
c)
Can‟t Say
29.
Which gradient technique is more advantageous when the data is too big to handle in RAM simultaneously?
a)
Full Batch Gradient Descent
b)
Stochastic Gradient Descent
30.
What are the factors to select the depth of neural network?1. Type of neural network (eg. MLP, CNN etc)
2. Input data
3. Computation power, i.e. Hardware capabilities and software capabilities
4. Learning Rate
5. The output function to map
a)
1, 2, 4, 5
b)
2, 3, 4, 5
c)
1, 3, 4, 5
d)
All of these
31.
Consider the scenario. The problem you are trying to solve has a small amount of data. Fortunately, you have a pre-trained neural network that was trained on a similar problem. Which of the following methodologies would you choose to make use of this pre-trained network?
a)
Re-train the model for the new dataset
b)
Assess on every layer how the model performs and only select a few of them
c)
) Fine tune the last couple of layers only
d)
Freeze all the layers except the last, re-train the last layer
32.
.Increase in size of a convolutional kernel would necessarily increase the performance of a convolutional network
a)
True
b)
False
100 %
