UNIT 1
Table of Contents
1. **Introduction to Artificial Intelligence and Intelligent Systems:**
Which of the following best describes an intelligent agent in the context of Artificial Intelligence?
**Answer:** a) A software program that can perform tasks autonomously
2. What is the primary goal of search methods in Artificial Intelligence?
**Answer:** c) To efficiently navigate through a problem space to find a solution
3. Which of the following is a common form of knowledge representation in AI?
**Answer:** c) Expert systems
4What are some typical use cases of Artificial Intelligence?
a) Natural Language Processing
b) Computer Vision
c) Robotics
d) All of the above
Answer: d) All of the above
5. What is the role of a Machine Learning Engineer in AI development?
**Answer:** a) Designing and implementing machine learning algorithms
6. **Python for Data Analysis:**
7. Which Python data structure is optimized for numerical operations and efficient array handling?
**Answer:** c) Arrays
8. What is the purpose of the NumPy library in Python?
**Answer:** b) Scientific computing and numerical operations
9. Which library in Python is commonly used for data manipulation and analysis?
**Answer:** c) Pandas
10. Which of the following is NOT a data visualization library in Python?
**Answer:** c) NumPy
11. What is the primary role of Matplotlib and Seaborn libraries in Python?
**Answer:** c) Data visualization
12. In an informed search, which of the following is true?
**Answer:** b) It uses problem-specific knowledge beyond the definition of the problem itself.
13. What is the function of backtracking in search algorithms?
**Answer:** a) To return to previous choices when the current path fails to reach a solution.
14. Which search algorithm guarantees the shortest path in terms of the number of edges?
**Answer:** d) Dijkstra’s algorithm
15. What does the term “heuristic” mean in the context of search algorithms?
**Answer:** a) A technique used to solve problems more quickly when classic methods are too slow.
16. Which knowledge representation technique is based on rules and facts about a domain?
**Answer:** c) Logical representation
17. What is the primary purpose of machine learning?
**Answer:** d) To enable computers to learn from data without being explicitly programmed.
18. What does the term “supervised learning” mean in machine learning?
**Answer:** b) Learning from labeled data, with input-output pairs provided.
19. Which of the following is NOT a classification algorithm?
**Answer:** d) K-means clustering
20. In Python, what does the term “import pandas as pd” accomplish?
**Answer:** a) It imports the Pandas library and assigns it the alias “pd” for easier use in code.
21. Which Pandas function is used to drop rows with missing values from a DataFrame?
**Answer:** b) dropna()
22. What does the function “df.head()” do when applied to a Pandas DataFrame?
**Answer:** c) It displays the first few rows of the DataFrame.
23. Which library in Python is primarily used for creating interactive visualizations?
**Answer:** d) Plotly
24. What is the purpose of the “plt.savefig()” function in Matplotlib?
**Answer:** a) It saves the current figure to a file.
25. What does the Seaborn library provide in addition to Matplotlib?
**Answer:** b) High-level interface for drawing attractive and informative statistical graphics.
26. Which Python data structure is immutable?
**Answer:** b) Tuples
27. What is the purpose of the “reshape()” function in NumPy?
**Answer:** c) To change the shape of an array without changing its data.
28. Which Pandas function is used to merge two DataFrames based on a common column?
**Answer:** d) merge()
29. What is the main advantage of using vectorized operations with NumPy arrays?
**Answer:** a) Improved computational efficiency compared to using loops.
30. Which of the following is NOT a type of machine learning?
**Answer:** c) Deterministic learning
31. What does the acronym “AI” stand for?
**Answer:** b) Artificial Intelligence
32. Which of the following is a characteristic of an intelligent agent?
**Answer:** d) All of the above (reactivity, autonomy, adaptability)
33. Which search algorithm is commonly used to find the shortest path in a weighted graph?
**Answer:** c) A* search
34. What is the role of knowledge representation in artificial intelligence?
**Answer:** a) To capture and organize information for reasoning and problem-solving.
35. Which Python library is commonly used for natural language processing (NLP)?
**Answer:** a) NLTK (Natural Language Toolkit)
36. What does the term “Big Data” refer to?
**Answer:** b) Extremely large datasets that may be analyzed computationally to reveal patterns, trends, and associations.
37. What is the purpose of a decision tree in machine learning?
**Answer:** b) To make decisions by following a series of criteria based on features of the input data.
38. Which of the following is NOT a step in the machine learning process?
**Answer:** d) Debugging
39. What is the primary function of the “train_test_split()” function in machine learning?
**Answer:** b) To split a dataset into separate training and testing sets.
40. Which Python library is commonly used for deep learning?
**Answer:** c) TensorFlow
41. What does the term “overfitting” mean in machine learning?
**Answer:** a) When a model learns the training data too well, including noise and random fluctuations.
42. What is the primary purpose of regularization in machine learning?
**Answer:** c) To prevent overfitting by adding a penalty term to the loss function.
43. Which of the following is a supervised learning task?
**Answer:** a) Regression
44. What is the primary purpose of cross-validation in machine learning?
**Answer:** b) To assess how well a model generalizes to new data by splitting the dataset into multiple subsets for training and testing.
45. Which of the following is NOT a type of ensemble learning?
**Answer:** d) Support Vector Machines
46. What is the main advantage of using Python for data analysis?
**Answer:** a) Rich ecosystem of libraries and tools for various tasks such as data manipulation, visualization, and machine learning.
47. Which Python library provides tools for working with large multi-dimensional arrays and matrices?
**Answer:** a) NumPy
48. What does the “iloc” attribute in Pandas DataFrame stand for?
**Answer:** c) Integer location, used for selecting data by index position.
49. What is the primary purpose of regular expressions in Python?
**Answer:** b) Pattern matching and text manipulation.
50. What is the purpose of the “apply()” function in Pandas?
**Answer d) To apply a function along an axis of a DataFrame.
UNIT 2
Data Wrangling Techniques:
What is the primary goal of data pre-processing in machine learning?
a) To increase the complexity of the dataset
b) To reduce the size of the dataset
c) To clean and prepare the dataset for analysis
d) To visualize the dataset
Answer: c) To clean and prepare the dataset for analysis
Which technique is commonly used for handling missing data in a dataset?
a) Dropping the missing values
b) Filling missing values with the mean or median
c) Ignoring missing values during analysis
d) Creating synthetic data to replace missing values
Answer: b) Filling missing values with the mean or median
What is the purpose of splitting the data into train and test sets?
a) To increase computational efficiency
b) To create duplicate datasets for redundancy
c) To evaluate the performance of a machine learning model
d) To combine multiple datasets for analysis
Answer: c) To evaluate the performance of a machine learning model
Which of the following is a common feature scaling technique?
a) Z-score normalization
b) Min-max scaling
c) Robust scaling
d) All of the above
Answer: d) All of the above
What does the Bag of Words model represent in Natural Language Processing?
a) The grammatical structure of sentences
b) The frequency of words in a document
c) The sentiment of the text
d) The semantic meaning of words
Answer: b) The frequency of words in a document
Which library in Python is commonly used for data manipulation and analysis?
a) TensorFlow
b) Scikit-learn
c) Pandas
d) Matplotlib
Answer: c) Pandas
What method is commonly used for splitting a dataset into training and testing sets in Python?
a) train_test_split()
b) split_dataset()
c) divide_data()
d) train_validate_test_split()
Answer: a) train_test_split()
Which of the following is NOT a common technique for handling missing data?
a) Dropping the missing values
b) Filling missing values with the mode
c) Predicting missing values using regression
d) Replacing missing values with arbitrary constant
Answer: b) Filling missing values with the mode
What is the purpose of feature scaling in machine learning?
a) To normalize the range of features
b) To increase the number of features
c) To reduce the number of features
d) To replace missing values with zeros
Answer: a) To normalize the range of features
What is the purpose of feature engineering in machine learning?
a) To create new features from existing ones
b) To remove all features except the target variable
c) To visualize features in 3D space
d) To randomly select features for modeling
Answer: a) To create new features from existing ones
Natural Language Processing:
What is the primary goal of Natural Language Processing (NLP)?
a) To analyze and understand human languages by computers
b) To translate text from one language to another
c) To create grammatically correct sentences
d) To summarize large documents
Answer: a) To analyze and understand human languages by computers
Which technique is commonly used to convert text data into numerical format in NLP?
a) Tokenization
b) Normalization
c) Lemmatization
d) Stemming
Answer: a) Tokenization
What is the Bag of Words (BoW) model used for in NLP?
a) To represent text data as a collection of words without considering grammar or word order
b) To classify images based on textual descriptions
c) To translate text between languages
d) To generate human-like responses in chatbots
Answer: a) To represent text data as a collection of words without considering grammar or word order
Which Python library is commonly used for Natural Language Processing tasks such as tokenization and stemming?
a) NumPy
b) Pandas
c) NLTK (Natural Language Toolkit)
d) Scikit-learn
Answer: c) NLTK (Natural Language Toolkit)
What is sentiment analysis used for in Natural Language Processing?
a) Analyzing the structure of sentences
b) Predicting the emotional tone of a text
c) Translating text between languages
d) Summarizing long documents
Answer: b) Predicting the emotional tone of a text
Which of the following is NOT a common preprocessing step in NLP?
a) Lemmatization
b) Tokenization
c) Vectorization
d) Normalization
Answer: c) Vectorization
Which of the following is NOT a common application of NLP?
a) Sentiment analysis
b) Image recognition
c) Named entity recognition
d) Machine translation
Answer: b) Image recognition
What is the purpose of stemming in NLP?
a) To group words with similar meanings
b) To represent words as numerical vectors
c) To identify the grammatical structure of sentences
d) To reduce words to their base or root form
Answer: d) To reduce words to their base or root form
Which technique is used to determine the grammatical structure of sentences in NLP?
a) Tokenization
b) Parsing
c) Stemming
d) Lemmatization
Answer: b) Parsing
What role does Natural Language Processing play in chatbots?
a) Generating random responses
b) Understanding and responding to user queries
c) Analyzing audio files
d) Creating visualizations of textual data
Answer: b) Understanding and responding to user queries
Which of the following is NOT a preprocessing step in NLP?
a) Tokenization
b) Dimensionality reduction
c) Stopword removal
d) Lemmatization
Answer: b) Dimensionality reduction
What is the purpose of TF-IDF in NLP?
a) To measure the importance of a word in a document relative to a corpus
b) To perform topic modeling
c) To identify named entities in text
d) To translate text between languages
Answer: a) To measure the importance of a word in a document relative to a corpus
Which technique is used to represent words as dense numerical vectors in NLP?
a) One-hot encoding
b) Bag of Words (BoW)
c) Word embeddings
d) TF-IDF
Answer: c) Word embeddings
What is the purpose of named entity recognition (NER) in NLP?
a) To identify the grammatical structure of sentences
b) To classify text into predefined categories
c) To extract specific entities such as names, dates, and locations from text
d) To measure the similarity between documents
Answer: c) To extract specific entities such as names, dates, and locations from text
Which of the following techniques is commonly used for text summarization in NLP?
a) Named entity recognition
b) Bag of Words (BoW)
c) Word embeddings
d) TextRank algorithm
Answer: d) TextRank algorithm
What is the process of converting words to their base or root form called in NLP?
a) Tokenization
b) Lemmatization
c) Stemming
d) Parsing
Answer: b) Lemmatization
Which library in Python provides tools for natural language processing, including tokenization and stemming?
a) Pandas
b) NLTK
c) Matplotlib
d) Scikit-learn
Answer: b) NLTK
What is the purpose of the Bag of Words (BoW) model in NLP?
a) To represent the syntax of sentences
b) To classify documents based on word frequencies
c) To translate text between languages
d) To generate word embeddings
Answer: b) To classify documents based on word frequencies
Which of the following is NOT a common step in data pre-processing for NLP tasks?
a) Stopword removal
b) Lemmatization
c) Feature scaling
d) Tokenization
Answer: c) Feature scaling
What is the primary objective of sentiment analysis in NLP?
a) To classify text documents into categories
b) To measure the emotional tone of text
c) To summarize long documents
d) To translate text between languages
Answer: b) To measure the emotional tone of text
Which of the following techniques is used to convert text data into numerical form by representing each word as a unique integer?
a) Bag of Words (BoW)
b) TF-IDF
c) Word embeddings
d) One-hot encoding
Answer: d) One-hot encoding
In NLP, what does TF-IDF stand for?
a) Term Frequency-Inverse Document Frequency
b) Text Frequency-Integrated Document Factor
c) Token Frequency-Inverse Data Filter
d) Term Frequency-Indexed Document Format
Answer: a) Term Frequency-Inverse Document Frequency
Which of the following is NOT a common application of Natural Language Processing?
a) Speech recognition
b) Chatbots
c) Image classification
d) Machine translation
Answer: c) Image classification
What is the primary purpose of stemming in Natural Language Processing?
a) To group similar words together
b) To remove stopwords from text
c) To represent words as numerical vectors
d) To reduce words to their base form
Answer: d) To reduce words to their base form
Which of the following techniques is used to capture the semantic meaning of words in NLP?
a) Bag of Words (BoW)
b) Term Frequency-Inverse Document Frequency (TF-IDF)
c) Word embeddings
d) Lemmatization
Answer: c) Word embeddings
What is the process of converting words into their dictionary form called in NLP?
a) Tokenization
b) Lemmatization
c) Stemming
d) Parsing
Answer: b) Lemmatization
Which of the following is NOT a common step in text pre-processing for NLP?
a) Tokenization
b) Dimensionality reduction
c) Stopword removal
d) Lemmatization
Answer: b) Dimensionality reduction
What is the primary purpose of the Bag of Words (BoW) model in NLP?
a) To represent the structure of sentences
b) To capture the semantic meaning of words
c) To classify documents based on word frequencies
d) To perform sentiment analysis
Answer: c) To classify documents based on word frequencies
Which of the following is NOT a common step in data pre-processing for NLP tasks?
a) Tokenization
b) Lemmatization
c) Feature scaling
d) Stopword removal
Answer: c) Feature scaling
What is the primary objective of sentiment analysis in NLP?
a) To classify text documents into categories
b) To measure the emotional tone of text
c) To summarize long documents
d) To translate text between languages
Answer: b) To measure the emotional tone of text
Which of the following techniques is used to convert text data into numerical form by representing each word as a unique integer?
a) Bag of Words (BoW)
b) TF-IDF
c) Word embeddings
d) One-hot encoding
Answer: d) One-hot encoding
In NLP, what does TF-IDF stand for?
a) Term Frequency-Inverse Document Frequency
b) Text Frequency-Integrated Document Factor
c) Token Frequency-Inverse Data Filter
d) Term Frequency-Indexed Document Format
Answer: a) Term Frequency-Inverse Document Frequency
Which of the following is NOT a common application of Natural Language Processing?
a) Speech recognition
b) Chatbots
c) Image classification
d) Machine translation
Answer: c) Image classification
What is the primary purpose of stemming in Natural Language Processing?
a) To group similar words together
b) To remove stopwords from text
c) To represent words as numerical vectors
d) To reduce words to their base form
Answer: d) To reduce words to their base form
Which of the following techniques is used to capture the semantic meaning of words in NLP?
a) Bag of Words (BoW)
b) Term Frequency-Inverse Document Frequency (TF-IDF)
c) Word embeddings
d) Lemmatization
Answer: c) Word embeddings
What is the process of converting words into their dictionary form called in NLP?
a) Tokenization
b) Lemmatization
c) Stemming
d) Parsing
Answer: b) Lemmatization
Which of the following is NOT a common step in text pre-processing for NLP?
a) Tokenization
b) Dimensionality reduction
c) Stopword removal
d) Lemmatization
Answer: b) Dimensionality reduction
What is the primary purpose of the Bag of Words (BoW) model in NLP?
a) To represent the structure of sentences
b) To capture the semantic meaning of words
c) To classify documents based on word frequencies
d) To perform sentiment analysis
Answer: c) To classify documents based on word frequencies
Which of the following is NOT a common step in data pre-processing for NLP tasks?
a) Tokenization
b) Lemmatization
c) Feature scaling
d) Stopword removal
Answer: c) Feature scaling
What is the primary objective of sentiment analysis in NLP?
a) To classify text documents into categories
b) To measure the emotional tone of text
c) To summarize long documents
d) To translate text between languages
Answer: b) To measure the emotional tone of text
UNIT 3
1. What is the primary function of a neuron in a neural network?
a) Store data
b) Perform calculations
c) Transfer data
d) Generate random numbers
Answer: b) Perform calculations
2. Which of the following is commonly used as an activation function in neural networks?
a) Mean function
b) Sigmoid function
c) Mode function
d) Median function
Answer: b) Sigmoid function
3. What does the process of backpropagation involve in neural networks?
a) Propagating errors backwards through the network to update weights
b) Propagating signals forwards through the network for prediction
c) Updating the activation function
d) Randomly initializing the network weights
Answer: a) Propagating errors backwards through the network to update weights
4. What is gradient descent used for in neural networks?
a) Calculating the activation of neurons
b) Initializing the weights of the network
c) Optimizing the network’s performance by adjusting weights
d) Reducing the number of neurons in the network
Answer: c) Optimizing the network’s performance by adjusting weights
5. Which technique updates the weights of a neural network using a random subset of the training data?
a) Gradient Descent
b) Backpropagation
c) Stochastic Gradient Descent
d) Mini-batch Gradient Descent
Answer: c) Stochastic Gradient Descent
6. What is the primary purpose of the activation function in a neural network?
a) Normalize the output
b) Introduce non-linearity
c) Reduce the computational complexity
d) Speed up convergence
Answer: b) Introduce non-linearity
7. Which of the following is a common problem addressed by the activation function in neural networks?
a) Overfitting
b) Underfitting
c) Vanishing gradient
d) Exploding gradient
Answer: c) Vanishing gradient
8. What is the primary goal of gradient descent in neural networks?
a) Maximize the accuracy of the model
b) Minimize the loss function by adjusting weights
c) Increase the learning rate
d) Regularize the network
Answer: b) Minimize the loss function by adjusting weights
9. How does backpropagation help in training a neural network?
a) By updating the weights based on prediction error
b) By initializing the weights randomly
c) By adjusting the learning rate
d) By changing the activation function
Answer: a) By updating the weights based on prediction error
10. Which technique involves updating the weights of a neural network by computing gradients of the loss function?
a) Forward propagation
b) Backpropagation
c) Gradient descent
d) Activation function update
Answer: b) Backpropagation
11. What is the main advantage of Recurrent Neural Networks (RNNs) over feedforward neural networks?
a) RNNs can handle variable-length input sequences
b) RNNs have fewer parameters to train
c) RNNs are faster in training
d) RNNs can process data in parallel
Answer: a) RNNs can handle variable-length input sequences
12. What is the primary limitation of a standard feedforward neural network when dealing with sequential data?
a) It cannot handle variable-length sequences
b) It requires fewer parameters
c) It has faster training time
d) It has a simpler architecture
Answer: a) It cannot handle variable-length sequences
13. What problem does the vanishing gradient phenomenon cause in training Recurrent Neural Networks?
a) Slow convergence
b) Overfitting
c) Underfitting
d) Loss explosion
Answer: a) Slow convergence
14. Which technique is used to address the vanishing gradient problem in Recurrent Neural Networks?
a) Weight initialization
b) Gradient clipping
c) Learning rate decay
d) Dropout regularization
Answer: b) Gradient clipping
15. What is the purpose of Long Short-Term Memory (LSTM) cells in Recurrent Neural Networks?
a) To reduce memory consumption
b) To increase the number of layers
c) To capture long-term dependencies
d) To speed up training
Answer: c) To capture long-term dependencies
16. How do LSTMs address the vanishing gradient problem in Recurrent Neural Networks?
a) By reducing the learning rate
b) By initializing weights randomly
c) By introducing gating mechanisms
d) By increasing the number of layers
Answer: c) By introducing gating mechanisms
17. What role do LSTMs play in predicting sequential data, such as stock prices?
a) They capture patterns over time
b) They reduce the dimensionality of the data
c) They normalize the data distribution
d) They generate random predictions
Answer: a) They capture patterns over time
18. What is the purpose of evaluating a Recurrent Neural Network?
a) To determine the number of layers
b) To assess its performance on unseen data
c) To initialize the weights
d) To select the activation function
Answer: b) To assess its performance on unseen data
19. How can the performance of a Recurrent Neural Network be improved?
a) By increasing the number of neurons
b) By reducing the learning rate
c) By adding more layers
d) By fine-tuning hyperparameters
Answer: d) By fine-tuning hyperparameters
20. What technique is used to fine-tune the parameters of a Recurrent Neural Network?
a) Weight initialization
b) Activation function update
c) Hyperparameter tuning
d) Gradient descent
Answer: c) Hyperparameter tuning
21. Which of the following is a common problem addressed by the activation function in Recurrent Neural Networks?
a) Overfitting
b) Underfitting
c) Vanishing gradient
d) Exploding gradient
Answer: c) Vanishing gradient
22. What is the primary goal of gradient descent in Recurrent Neural Networks?
a) Maximize the accuracy of the model
b) Minimize the loss function by adjusting weights
c) Increase the learning rate
d) Regularize the network
Answer: b) Minimize the loss function by adjusting weights
23. How does backpropagation help in training a Recurrent Neural Network?
a) By updating the weights based on prediction error
b) By initializing the weights randomly
c) By adjusting the learning rate
d) By changing the activation function
Answer: a) By updating the weights based on prediction error
24. Which technique updates the weights of a Recurrent Neural Network using a random subset of the training data?
a) Gradient Descent
b) Backpropagation
c) Stochastic Gradient Descent
d) Mini-batch Gradient Descent
Answer: c) Stochastic Gradient Descent
25. What is the primary purpose of the activation function in a Recurrent Neural Network?
a) Normalize the output
b) Introduce non-linearity
c) Reduce the computational complexity
d) Speed up convergence
Answer: b) Introduce non-linearity
26. What is the main advantage of Recurrent Neural Networks (RNNs) over feedforward neural networks?
a) RNNs can handle variable-length input sequences
b) RNNs have fewer parameters to train
c) RNNs are faster in training
d) RNNs can process data in parallel
Answer: a) RNNs can handle variable-length input sequences
27. What is the primary limitation of a standard feedforward neural network when dealing with sequential data?
a) It cannot handle variable-length sequences
b) It requires fewer parameters
c) It has faster training time
d) It has a simpler architecture
Answer: a) It cannot handle variable-length sequences
28. What problem does the vanishing gradient phenomenon cause in training Recurrent Neural Networks?
a) Slow convergence
b) Overfitting
c) Underfitting
d) Loss explosion
Answer: a) Slow convergence
29. Which technique is used to address the vanishing gradient problem in Recurrent Neural Networks?
a) Weight initialization
b) Gradient clipping
c) Learning rate decay
d) Dropout regularization
Answer: b) Gradient clipping
30. What is the purpose of Long Short-Term Memory (LSTM) cells in Recurrent Neural Networks?
a) To reduce memory consumption
b) To increase the number of layers
c) To capture long-term dependencies
d) To speed up training
Answer: c) To capture long-term dependencies
31. How do LSTMs address the vanishing gradient problem in Recurrent Neural Networks?
a) By reducing the learning rate
b) By initializing weights randomly
c) By introducing gating mechanisms
d) By increasing the number of layers
Answer: c) By introducing gating mechanisms
32. What role do LSTMs play in predicting sequential data, such as stock prices?
a) They capture patterns over time
b) They reduce the dimensionality of the data
c) They normalize the data distribution
d) They generate random predictions
Answer: a) They capture patterns over time
33. What is the purpose of evaluating a Recurrent Neural Network?
a) To determine the number of layers
b) To assess its performance on unseen data
c) To initialize the weights
d) To select the activation function
Answer: b) To assess its performance on unseen data
34. How can the performance of a Recurrent Neural Network be improved?
a) By increasing the number of neurons
b) By reducing the learning rate
c) By adding more layers
d) By fine-tuning hyperparameters
Answer: d) By fine-tuning hyperparameters
35. What technique is used to fine-tune the parameters of a Recurrent Neural Network?
a) Weight initialization
b) Activation function update
c) Hyperparameter tuning
d) Gradient descent
Answer: c) Hyperparameter tuning
36. Which of the following is a common problem addressed by the activation function in Recurrent Neural Networks?
a) Overfitting
b) Underfitting
c) Vanishing gradient
d) Exploding gradient
Answer: c) Vanishing gradient
37. What is the primary goal of gradient descent in Recurrent Neural Networks?
a) Maximize the accuracy of the model
b) Minimize the loss function by adjusting weights
c) Increase the learning rate
d) Regularize the network
Answer: b) Minimize the loss function by adjusting weights
38. How does backpropagation help in training a Recurrent Neural Network?
a) By updating the weights based on prediction error
b) By initializing the weights randomly
c) By adjusting the learning rate
d) By changing the activation function
Answer: a) By updating the weights based on prediction error
39. Which technique updates the weights of a Recurrent Neural Network using a random subset of the training data?
a) Gradient Descent
b) Backpropagation
c) Stochastic Gradient Descent
d) Mini-batch Gradient Descent
Answer: c) Stochastic Gradient Descent
40. What is the primary purpose of the activation function in a Recurrent Neural Network?
a) Normalize the output
b) Introduce non-linearity
c) Reduce the computational complexity
d) Speed up convergence
Answer: b) Introduce non-linearity
41. What is the main advantage of Recurrent Neural Networks (RNNs) over feedforward neural networks? a) RNNs can handle variable-length input sequences
b) RNNs have fewer parameters to train
c) RNNs are faster in training
d) RNNs can process data in parallel
Answer: a) RNNs can handle variable-length input sequences
42. What is the primary limitation of a standard feedforward neural network when dealing with sequential data? a) It cannot handle variable-length sequences
b) It requires fewer parameters
c) It has faster training time
d) It has a simpler architecture
Answer: a) It cannot handle variable-length sequences
43. What problem does the vanishing gradient phenomenon cause in training Recurrent Neural Networks? a) Slow convergence
b) Overfitting
c) Underfitting
d) Loss explosion
Answer: a) Slow convergence
44. Which technique is used to address the vanishing gradient problem in Recurrent Neural Networks? a) Weight initialization
b) Gradient clipping
c) Learning rate decay
d) Dropout regularization
Answer: b) Gradient clipping
45. What is the purpose of Long Short-Term Memory (LSTM) cells in Recurrent Neural Networks? a) To reduce memory consumption
b) To increase the number of layers
c) To capture long-term dependencies
d) To speed up training
Answer: c) To capture long-term dependencies
41. What is the main advantage of Recurrent Neural Networks (RNNs) over feedforward neural networks?
a) RNNs can handle variable-length input sequences
b) RNNs have fewer parameters to train
c) RNNs are faster in training
d) RNNs can process data in parallel
Answer: a) RNNs can handle variable-length input sequences
42. What is the primary limitation of a standard feedforward neural network when dealing with sequential data?
a) It cannot handle variable-length sequences
b) It requires fewer parameters
c) It has faster training time
d) It has a simpler architecture
Answer: a) It cannot handle variable-length sequences
43. What problem does the vanishing gradient phenomenon cause in training Recurrent Neural Networks?
a) Slow convergence
b) Overfitting
c) Underfitting
d) Loss explosion
Answer: a) Slow convergence
44. Which technique is used to address the vanishing gradient problem in Recurrent Neural Networks?
a) Weight initialization
b) Gradient clipping
c) Learning rate decay
d) Dropout regularization
Answer: b) Gradient clipping
45. What is the purpose of Long Short-Term Memory (LSTM) cells in Recurrent Neural Networks?
a) To reduce memory consumption
b) To increase the number of layers
c) To capture long-term dependencies
d) To speed up training
Answer: c) To capture long-term dependencies
46. How do LSTMs address the vanishing gradient problem in Recurrent Neural Networks?
a) By reducing the learning rate
b) By initializing weights randomly
c) By introducing gating mechanisms
d) By increasing the number of layers
Answer: c) By introducing gating mechanisms
47. What role do LSTMs play in predicting sequential data, such as stock prices?
a) They capture patterns over time
b) They reduce the dimensionality of the data
c) They normalize the data distribution
d) They generate random predictions
Answer: a) They capture patterns over time
48. What is the purpose of evaluating a Recurrent Neural Network?
a) To determine the number of layers
b) To assess its performance on unseen data
c) To initialize the weights
d) To select the activation function
Answer: b) To assess its performance on unseen data
49. How can the performance of a Recurrent Neural Network be improved?
a) By increasing the number of neurons
b) By reducing the learning rate
c) By adding more layers
d) By fine-tuning hyperparameters
Answer: d) By fine-tuning hyperparameters
50. What technique is used to fine-tune the parameters of a Recurrent Neural Network?
a) Weight initialization
b) Activation function update
c) Hyperparameter tuning
d) Gradient descent
Answer: c) Hyperparameter tuning
Understanding Identifiers in C++ Programming: An Introduction to Naming Variables, Functions, and Labels
Table of Contents What is identifiers in C++ Programming ? Identifiers in C++ serve as unique names that identify various programming elements like variables, functions, classes, and labels. They are essential for referencing these elements within a program, enabling the
What is C++ Programming? A Historical Overview and Introduction to the Fundamentals and Features
History of C++ Programming The history of C++ programming dates back to the late 1970s when Bjarne Stroustrup, a Danish computer scientist, developed it as an extension of the C language. The main objective was to enhance C by incorporating
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