t/f A neural network with multiple hidden layers and sigmoid nodes can form non-linear decision boundaries. correct answers true
t/f If you increase the number of hidden layers in a Multi Layer Perceptron neural networks (fully connected networks), the classification error of test data always d...
Machine Learning Exam
t/f A neural network with multiple hidden layers and sigmoid nodes can form non-linear
decision boundaries. correct answers true
t/f If you increase the number of hidden layers in a Multi Layer Perceptron neural
networks (fully connected networks), the classification error of test data always
decreases. correct answers false
t/f The number of neurons in the output layer must match the number of classes (Where
the number of classes is greater than 2) in a supervised learning task. correct answers
false
t/f using Mini-batch gradient decent the model update frequency is higher than batch
gradient descent which allows for more robust convergence, avoiding local minima.
correct answers true
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 correct answers (b) Rectified Linear Unit
For a classification task, instead of random weight initializations in a neural network, we
set all the weights to zero. Which of the following statements is true?
(a) There will not be any problem and the neural network will train properly
(b) The neural network will train but all the neurons will end up recognizing the same
thing
(c) The neural network will not train as there is no net gradient change
(d) None of these correct answers (b) The neural network will train but all the neurons
will end up recognizing the same thing
What are the steps for using a gradient descent algorithm in training neural networks?
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.
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 hope 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 to make them better
(d) None of these correct answers (c) Iteratively check that after assigning a value how
far you are from the best values, and slightly change the assigned values to make them
better
t/f A perceptron is guaranteed to perfectly learn a given linearly separable function
within a finite number of training steps. correct answers true
t/f A multiple-layer neural network with linear activation functions is equivalent to one
single-layer perceptron that uses the same error function on the output layer and has
the same number of inputs. correct answers true
Which of the following is NOT correct about ReLU (Rectified Linear Unit) activation
function?
(a) Does not saturate in the positive region.
(b) Computationally effective comparing to sigmoid and Tanh activation functions
(c) Mostly converge faster than sigmoid and Tanh activation functions
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