WorksheetsDeep Learning Quiz 2
Total questions: 20
Worksheet time: 23mins
Which of the following is NOT supervised learning?
Decision Tree
Linear Regression
Naive Bayes
Clustering
A Machine Learning method that is concerned with how software agents should take actions in an environment is
Reinforcement learning
semi- supervised learning
unsupervised learning
supervised learning
What is the objective of backpropagation algorithm?
to develop learning algorithm for multilayer feedforward neural network
to develop learning algorithm for single layer feedforward neural network
to propagate the computed loss to compute gradient
all the mentioned
What are general limitations of back propagation rule? Pick the right choice from the given options
i) local minima problem
ii) slow convergence
iii) scaling
only i
both ii and i
both ii and iii
i, ii and iii
Feedback networks are used for?
auto association
pattern storage
both auto association & pattern storage
pattern recognition
Number of output cases depends on what factor?
number of inputs
number of distinct classes
total number of classes
none of the mentioned
_________ is used to find local minima of the cost function
stochastic gradient descent
gradient descent
linear regression
logistic regression
Cost function(J) of Linear Regression is the _______ value between predicted y value (predicted) and true y value (y)
Mean
Root mean square
Median
Mean square
A = 1/(1 + e-x) is an equation representing which activation function?
ReLU
Sigmoid
Leaky ReLu
Tanh
The algorithm creates a line or a hyperplane which separates the data into classes and also suitable for classification and regression
K-means clustering
Support vector machine
Bayesian inference
perceptron
What steps can we take to prevent overfitting in a Neural Network?
Data Augmentation
Early Stopping
Dropout
Regularization
All the mentioned
Identify the activation function from the given diagram
Sigmoid, ReLU
ReLU, Leaky ReLU
ReLU, Tanh
TanH, ReLU
___________uses the processing of the brain as a basis to develop algorithms that can be used to model complex patterns and prediction problems.
ANN
CNN
RNN
KNN
How many possible layers can be there in deep neural network
1
≥ 50
10
no limit
In VC dimensions of neural networks what does VC stand for?
Vladimir Cherubim
Vapnik Chervonenkis
Victor Charlie
Vanessa Carlton
The number of nodes in the input layer is 10 and the hidden layer is 5. The maximum number of connections from the input layer to the hidden layer are
50
Less than 50
More than 50
It is an arbitrary value
In a simple MLP model with 8 neurons in the input layer, 5 neurons in the hidden layer and 1 neuron in the output layer. What is the size of the weight matrices between hidden output layer and input hidden layer?
[1 X 5] , [5 X 8]
[8 X 5] , [ 1 X 5]
[8 X 5] , [5 X 1]
[5 x 1] , [8 X 5]
Which of the following neural network training challenge can be solved using batch normalization?
Overfitting
Restrict activations to become too high or low
Training is too slow
All the mentioned
For a binary classification problem, which of the following architecture would you choose?
1
2
Any one of these
None of these
The red curve above denotes training accuracy with respect to each epoch in a deep learning algorithm. Both the green and blue curves denote validation accuracy.
Green Curve
Blue Curve
Red Curve
None
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