1. What is the primary goal of Greedy Layerwise Pre-training in deep learning?  
2 points
A) To minimize the training time of the entire network
B) To optimize the weights of the entire network simultaneously
C) To initialize the weights of each layer in a way that improves training
D) To reduce overfitting by using dropout layers
2.  What is one of the main benefits of using Greedy Layerwise Pre-training?
2 points
A) It allows for training on labeled data only.
B) It reduces the need for large labeled datasets.
C) It increases the complexity of the model.
D) It eliminates the vanishing gradient problem entirely.
3. What is a common challenge faced in Domain Adaptation?
2 points
A) Lack of pre-trained models
B) The difference in feature distributions between the source and target domains
C) Overfitting on the target domain
D) Insufficient data for training
4. Which of the following methods can be used for Domain Adaptation?
2 points
A) Data augmentation
B) Feature alignment
C) Transfer of hyperparameters
D) Model pruning
5.  Which of the following is a common application of distributed representations?
2 points
A) Document clustering
B) Image recognition
C) Word embeddings, such as Word2Vec
D) Reinforcement learning
6. Which technique is commonly used to visualize high-dimensional distributed representations?
2 points
A) K-means clustering
B) Principal Component Analysis (PCA)
C) Decision trees
D) Support Vector Machines (SVM)
7. What is the primary benefit of using DenseNet architecture?
2 points
A) It requires less data for training.
B) It reduces the number of parameters while maintaining performance.
C) It simplifies the model architecture significantly.
D) It eliminates the need for dropout layers.
Option 5
8. Which of the following is a variant of DenseNet?
2 points
A) ResNet
B) Inception
C) DenseNet-121
D) AlexNet
9.In the context of natural language processing, deep learning is often used for:
2 points
A) Data encryption
B) Sentiment analysis
C) Database management
D) Network security
10.Which of the following is a common application of deep learning in computer vision?
2 points
A) Natural Language Processing (NLP)
B) Image classification
C) Time series forecasting
D) Reinforcement learning
11. Which of the following techniques is commonly used in modern speech recognition systems?
2 points
A) Linear regression
B) Hidden Markov Models (HMMs)
C) Decision trees
D) K-means clustering
12. What role do "feature extraction" techniques play in speech recognition?
2 points
A) They generate synthetic speech.
B) They convert speech signals into a format suitable for processing.
C) They improve the accuracy of text-to-speech systems.
D) They help in noise cancellation.
  13.What is a common technique used for representing words in NLP?  
2 points
A) One-hot encoding
B) Binary encoding
C) Word embeddings (e.g., Word2Vec, GloVe)
D) Linear regression
14. What is "tokenization" in the context of NLP?
2 points
A) The process of translating text to another language
B) The division of text into smaller units, such as words or phrases
C) The summarization of large text documents
D) The analysis of the sentiment of a piece of text
15. Which of the following is a common technique used in collaborative filtering?
2 points
A) Content-based filtering
B) Matrix factorization
C) Clustering
D) Decision trees