Question 1

What is the purpose of dimensionality reduction in machine learning?

Options :

A : Data Augmentation

B : Model Evaluation

C : Model Regularization

D : Feature Extraction

E : Model Interpretation

F : Noise Reduction

Answer: D

Question 2

How does batch normalization help in training deep neural networks?

Options :

A : By reducing overfitting

B : By speeding up convergence

C : By increasing model accuracy

D : By simplifying the model architecture

Answer: B

Question 3

Which of the following is NOT a common technique for improving the performance of classification models?

Options :

A : Feature Engineering

B : Ensemble Learning

C : Hyperparameter Tuning

D : Regularization

E : Gradient Descent

F : Dimensionality Reduction

Answer: E

Question 4

Which of these is a benefit of using convolutional neural networks (CNNs) for image processing?

Options :

A : They require smaller datasets

B : They are less computationally intensive

C : They automatically detect important features

D : They do not require labeled data

Answer: C

Question 5

What is the primary purpose of the softmax activation function in the output layer of a neural network for multi-class classification?

Options :

A : Reduce Model Complexity

B : Introduce Non-Linearity

C : Scale Input Features

D : Speed Up Convergence

E : Normalize Data

F : Compute Class Probabilities

Answer: F