CS7643 Deep Learning – Quiz 2: High-Yield
100-Question Real Exam with Answers &
100%CORRET ANSWERs
Overview:
This comprehensive 100-question practice exam is designed for students preparing for CS7643
– Deep Learning (Quiz 2). It focuses on the most-tested concepts, frequently searched topics,
and high-yield questions, providing a realistic preparation tool.
Key topics covered include:
• Neural Network Fundamentals: MLPs, activation functions (ReLU, Sigmoid, Tanh),
weight initialization (Xavier, He), and loss functions (Cross-Entropy, MSE, Hinge).
• Optimization Techniques: SGD, Momentum, RMSProp, Adam, learning rate strategies,
and gradient issues (vanishing/exploding gradients).
• Convolutional Neural Networks (CNNs): Convolution, kernel/stride/padding, pooling
layers, and parameter counting.
• Recurrent Neural Networks (RNNs): Vanilla RNN, LSTM, GRU, gating mechanisms,
and BPTT.
• Regularization and Stabilization: Dropout, L1/L2 regularization, Batch Normalization,
and residual connections.
• Practical PyTorch Concepts: Forward/backward passes, layer parameters, and
implementation best practices.
Each question includes answers in bold and 100%CORRET ANSWERs
1. Xavier initialization is best for which activation functions?
A. ReLU
B. Sigmoid and Tanh
C. Softmax
D. Leaky ReLU
100%CORRET ANSWER: Xavier keeps activations’ variance stable for zero-
centered activations like sigmoid and tanh.
,2. He initialization is preferred for:
A. Sigmoid
B. ReLU
C. Tanh
D. Softmax
100%CORRET ANSWER: He accounts for ReLU’s positive slope to maintain
variance.
3. Which activation is zero-centered?
A. Sigmoid
B. Tanh
C. ReLU
D. Softmax
100%CORRET ANSWER: Tanh outputs range (-1,1), centering the data.
4. Sigmoid output range:
A. (-1,1)
B. (0, ∞)
C. (0,1)
D. (-∞, ∞)
100%CORRET ANSWER: Sigmoid maps any real number to (0,1), suitable for
probabilities.
5. ReLU derivative for x > 0:
A. 0
B. 1
C. x
D. Undefined
, 100%CORRET ANSWER: ReLU(x)=x for x>0, derivative =1.
6. Cross-entropy loss is used for:
A. Regression
B. Classification
C. Clustering
D. Autoencoders
100%CORRET ANSWER: Cross-entropy measures distance between predicted
probabilities and true labels.
7. Mean Squared Error (MSE) is used for:
A. Classification
B. Regression
C. Softmax
D. Hinge loss
100%CORRET ANSWER: MSE calculates squared difference for continuous
outputs.
8. Which optimizer adapts learning rates per parameter?
A. SGD
B. Momentum
C. Adam
D. RMSProp
100%CORRET ANSWER: Adam uses first and second moments to adjust
learning rates individually.
9. Momentum in gradient descent helps:
A. Prevent overfitting
B. Reduce batch size
100-Question Real Exam with Answers &
100%CORRET ANSWERs
Overview:
This comprehensive 100-question practice exam is designed for students preparing for CS7643
– Deep Learning (Quiz 2). It focuses on the most-tested concepts, frequently searched topics,
and high-yield questions, providing a realistic preparation tool.
Key topics covered include:
• Neural Network Fundamentals: MLPs, activation functions (ReLU, Sigmoid, Tanh),
weight initialization (Xavier, He), and loss functions (Cross-Entropy, MSE, Hinge).
• Optimization Techniques: SGD, Momentum, RMSProp, Adam, learning rate strategies,
and gradient issues (vanishing/exploding gradients).
• Convolutional Neural Networks (CNNs): Convolution, kernel/stride/padding, pooling
layers, and parameter counting.
• Recurrent Neural Networks (RNNs): Vanilla RNN, LSTM, GRU, gating mechanisms,
and BPTT.
• Regularization and Stabilization: Dropout, L1/L2 regularization, Batch Normalization,
and residual connections.
• Practical PyTorch Concepts: Forward/backward passes, layer parameters, and
implementation best practices.
Each question includes answers in bold and 100%CORRET ANSWERs
1. Xavier initialization is best for which activation functions?
A. ReLU
B. Sigmoid and Tanh
C. Softmax
D. Leaky ReLU
100%CORRET ANSWER: Xavier keeps activations’ variance stable for zero-
centered activations like sigmoid and tanh.
,2. He initialization is preferred for:
A. Sigmoid
B. ReLU
C. Tanh
D. Softmax
100%CORRET ANSWER: He accounts for ReLU’s positive slope to maintain
variance.
3. Which activation is zero-centered?
A. Sigmoid
B. Tanh
C. ReLU
D. Softmax
100%CORRET ANSWER: Tanh outputs range (-1,1), centering the data.
4. Sigmoid output range:
A. (-1,1)
B. (0, ∞)
C. (0,1)
D. (-∞, ∞)
100%CORRET ANSWER: Sigmoid maps any real number to (0,1), suitable for
probabilities.
5. ReLU derivative for x > 0:
A. 0
B. 1
C. x
D. Undefined
, 100%CORRET ANSWER: ReLU(x)=x for x>0, derivative =1.
6. Cross-entropy loss is used for:
A. Regression
B. Classification
C. Clustering
D. Autoencoders
100%CORRET ANSWER: Cross-entropy measures distance between predicted
probabilities and true labels.
7. Mean Squared Error (MSE) is used for:
A. Classification
B. Regression
C. Softmax
D. Hinge loss
100%CORRET ANSWER: MSE calculates squared difference for continuous
outputs.
8. Which optimizer adapts learning rates per parameter?
A. SGD
B. Momentum
C. Adam
D. RMSProp
100%CORRET ANSWER: Adam uses first and second moments to adjust
learning rates individually.
9. Momentum in gradient descent helps:
A. Prevent overfitting
B. Reduce batch size
