☰
Take a Quiz Test
Quiz Category
Machine Learning
Supervised Learning
Classification
Regression
Time Series Forecasting
Unsupervised Learning
Clustering
K-Means Clustering
Hierarchical Clustering
Semi-Supervised Learning
Reinforcement Learning(ML)
Deep Learning(ML)
Transfer Learning(ML)
Ensemble Learning
Explainable AI (XAI)
Bayesian Learning
Decision Trees
Support Vector Machines (SVMs)
Instance-Based Learning
Rule-Based Learning
Neural Networks
Evolutionary Algorithms
Meta-Learning
Multi-Task Learning
Metric Learning
Few-Shot Learning
Adversarial Learning
Data Pre Processing
Natural Language Processing(ML)
Machine Learning Quiz Questions
1.
Which of the following is a key component of Matching Networks in few-shot learning?
A. A memory-augmented neural network
B. A prototypical network
C. An attention mechanism
D. A Siamese network
view answer:
C. An attention mechanism
Explanation:
Matching Networks utilize an attention mechanism to weigh the importance of support set examples when classifying a query example.
2.
What is the main idea behind Siamese Networks in few-shot learning?
A. Learning a separate model for each class
B. Learning a shared representation for all classes
C. Learning to compare the similarity between pairs of examples
D. Learning a hierarchical structure of classes
view answer:
C. Learning to compare the similarity between pairs of examples
Explanation:
Siamese Networks learn to compare the similarity between pairs of examples by learning a shared representation and a similarity metric.
3.
What is the main idea behind Prototypical Networks in few-shot learning?
A. Learning a separate model for each class
B. Learning a shared representation for all classes
C. Learning class prototypes to represent each category
D. Learning a hierarchical structure of classes
view answer:
C. Learning class prototypes to represent each category
Explanation:
Prototypical Networks learn class prototypes to represent each category, and new examples are classified based on their similarity to these prototypes.
4.
What is the role of the query set in few-shot learning?
A. To train the model on a new task
B. To provide additional information for the model
C. To evaluate the model's performance on the new task
D. To store and retrieve information during training
view answer:
C. To evaluate the model's performance on the new task
Explanation:
The query set is used to evaluate the model's performance on the new task after adapting to it using the support set in few-shot learning.
5.
In few-shot learning, what is the purpose of a support set?
A. To provide a large set of examples for training
B. To provide a small set of examples for model adaptation
C. To evaluate the model's generalization performance
D. To store and retrieve information during training
view answer:
B. To provide a small set of examples for model adaptation
Explanation:
The support set is a small set of labeled examples used to adapt the model to a new task during few-shot learning.
6.
Which of the following is a distance metric used in few-shot learning?
A. Euclidean distance
B. Manhattan distance
C. Cosine similarity
D. All of the above
view answer:
D. All of the above
Explanation:
Distance metrics like Euclidean distance, Manhattan distance, and cosine similarity are used in few-shot learning to measure the similarity between examples.
7.
In the context of few-shot learning, what does N-way K-shot classification mean?
A. Classifying K examples into N categories
B. Classifying N examples into K categories
C. Classifying K examples with N features
D. Classifying N examples with K features
view answer:
A. Classifying K examples into N categories
Explanation:
N-way K-shot classification refers to the problem of classifying K examples into N different categories using limited labeled examples.
8.
What is the purpose of a memory-augmented neural network (MANN) in few-shot learning?
A. To store and retrieve information for improved generalization
B. To increase the model capacity
C. To reduce the training time
D. To improve model interpretability
view answer:
A. To store and retrieve information for improved generalization
Explanation:
Memory-augmented neural networks are designed to store and retrieve information effectively, which helps the model generalize better when given limited examples.
9.
What does "zero-shot learning" refer to?
A. Learning from no labeled examples
B. Learning from a single labeled example
C. Learning from a very large dataset
D. Learning without any prior knowledge
view answer:
A. Learning from no labeled examples
Explanation:
Zero-shot learning refers to the ability of a model to generalize to new tasks without any labeled examples from those tasks.
10.
Which of the following is an advantage of few-shot learning?
A. Reduces the need for large labeled datasets
B. Increases model complexity
C. Improves model interpretability
D. Reduces the risk of overfitting
view answer:
A. Reduces the need for large labeled datasets
Explanation:
Few-shot learning aims to reduce the need for large labeled datasets by enabling models to generalize from a small number of examples.
11.
Which of the following is a popular technique used in Few-Shot Learning?
A. Dropout
B. Data augmentation
C. Meta-learning
D. Batch normalization
view answer:
C. Meta-learning
Explanation:
Meta-learning is a popular technique in few-shot learning as it trains models to learn from small datasets by exposing them to a variety of tasks during training.
12.
What is the main goal of Meta-Learning?
A. To optimize the learning rate
B. To learn a good representation for the task
C. To learn how to learn
D. To avoid overfitting
view answer:
C. To learn how to learn
Explanation:
Meta-learning aims to learn how to learn, enabling models to quickly adapt to new tasks with minimal supervision.
13.
Which of the following is an example of a meta-learning algorithm?
A. MAML (Model-Agnostic Meta-Learning)
B. LASSO (Least Absolute Shrinkage and Selection Operator)
C. AdaBoost
D. Random Forest
view answer:
A. MAML (Model-Agnostic Meta-Learning)
Explanation:
MAML is a meta-learning algorithm that learns a model initialization that can be quickly fine-tuned for new tasks with few examples.
14.
What is Few-Shot Learning?
A. Learning from a large number of labeled examples
B. Learning from a small number of labeled examples
C. Learning from unlabeled data
D. Learning from human demonstrations
view answer:
B. Learning from a small number of labeled examples
Explanation:
Few-shot learning refers to training a machine learning model using a small number of labeled examples to quickly generalize and adapt to new tasks.
15.
What is the primary challenge in Few-Shot Learning?
A. Overfitting
B. Underfitting
C. High computation cost
D. Scalability
view answer:
A. Overfitting
Explanation:
Overfitting is the primary challenge in few-shot learning as models tend to memorize the few available examples rather than generalizing to new, unseen data.
‹
1
2
...
5
6
7
8
9
10
11
...
54
55
›
© aionlinecourse.com All rights reserved.