Practice
1. In the context of Word2Vec, which of the following best describes the purpose of the 'skip-gram' model?
easy
Solution
Step 1: Understand skip-gram model purpose
The skip-gram model in Word2Vec is designed to predict surrounding words given a target word.Step 2: Eliminate wrong options
Predicts the next word in a sequence given the previous words. describes a language model task, Clusters words into predefined categories. is about clustering, and Generates a sequence of words from a given context. is about sequence generation, none of which are the purpose of skip-gram.Final Answer:
Option D -> Option DQuick Check:
Skip-gram predicts context from target [OK]
Hint: Skip-gram predicts context from target [OK]
Common Mistakes:
2. What will be the output of the following Python code snippet using a linear kernel in SVM?
from sklearn.svm import SVC
import numpy as np
X = np.array([[1, 2], [2, 3], [3, 4]])
y = np.array([0, 1, 0])
model = SVC(kernel='linear')
model.fit(X, y)
print(model.support_vectors_)easy
Solution
Step 1: Understand the code execution
The code fits an SVM model with a linear kernel and prints the support vectors.Step 2: Eliminate wrong options
An error due to incorrect kernel specification is incorrect because 'linear' is a valid kernel. The coefficients of the decision boundary is incorrect because the code does not print coefficients. The predicted labels for the input data is incorrect because the code does not predict labels.Final Answer:
Option C -> Option CQuick Check:
Prints support vectors, not predictions [OK]
Hint: Prints support vectors, not predictions [OK]
Common Mistakes:
3. When choosing between LSTM and GRU for a time series prediction task with limited computational resources, which is generally more appropriate and why?
medium
Solution
Step 1: Compare LSTM and GRU.
GRU is simpler and often faster due to fewer parameters, making it suitable for limited resources.Step 2: Eliminate wrong options.
LSTM's complexity isn't always needed, GRU isn't always more accurate, and LSTM's longer sequence handling isn't a universal advantage.Final Answer:
Option C -> Option CQuick Check:
GRU: fewer parameters, faster. [OK]
Hint: GRU: fewer parameters, faster. [OK]
Common Mistakes:
4. A model is showing unexpectedly high validation accuracy but low test accuracy. What is the most likely cause?
medium
Solution
Step 1: Diagnose the issue
High validation accuracy but low test accuracy suggests the validation set may not be representative.Step 2: Eliminate wrong options
The model is underfitting the training data. would affect both validation and test accuracy, The test set is too similar to the training set. would not cause low test accuracy, and The model has too many parameters. would affect both validation and test accuracy similarly.Final Answer:
Option D -> Option DQuick Check:
Small validation set = misleading accuracy [OK]
Hint: Small validation set = misleading accuracy [OK]
Common Mistakes:
5. You are tasked with improving a model's performance on a highly imbalanced dataset. Which approach is most appropriate to prevent overfitting while addressing class imbalance?
hard
Solution
Step 1: Address class imbalance and overfitting
Class weights help balance the dataset, and early stopping prevents overfitting by halting training when validation performance stops improving.Step 2: Eliminate wrong options
Use SMOTE to oversample the minority class and increase model complexity. increases complexity, risking overfitting, Downsample the majority class and increase the learning rate. may lead to instability, and Use a simpler model with no regularization. lacks regularization, increasing overfitting risk.Final Answer:
Option C -> Option CQuick Check:
Class weights + early stopping = balanced, robust model [OK]
Hint: Class weights + early stopping = balanced, robust model [OK]
Common Mistakes: