Deep Learning Quiz | Generated by AI
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Machine Learning and Deep Learning Quiz
Multiple Choice Questions (MCQ):
- Which of the following best defines Machine Learning (ML)?
- A) A field of computer science focused on programming algorithms manually.
- B) A field of computer science that enables systems to learn from data and improve performance.
- C) A subset of statistics focused on hypothesis testing.
- D) A type of data visualization technique.
- What distinguishes Deep Learning (DL) from traditional Machine Learning (ML)?
- A) DL does not require any data preprocessing.
- B) DL uses neural networks with multiple layers to model complex patterns.
- C) ML always uses unsupervised learning.
- D) DL is not capable of solving image-related tasks.
- In Supervised Learning, what is used to train the model?
- A) Unlabeled data.
- B) Data with both input and associated correct output.
- C) Reinforcement signals.
- D) Pre-trained models from other tasks.
- What does the Transformer architecture primarily rely on for natural language processing?
- A) Convolution layers.
- B) Recurrent layers.
- C) Attention mechanisms.
- D) Decision trees.
- What is the main function of Positional Encoding in Transformers?
- A) To determine the importance of each word in the sequence.
- B) To help the model understand the order of tokens in a sequence.
- C) To compress input data.
- D) To ensure the model doesn’t overfit.
- Which model would you use to generate new, similar data like images or text?
- A) Generative Adversarial Network (GAN).
- B) Convolutional Neural Network (CNN).
- C) Decision Tree.
- D) K-Nearest Neighbors (KNN).
- What does the term “Zero-shot Learning” mean?
- A) Learning from only one labeled example.
- B) A model handling tasks without explicit training examples.
- C) A model performing tasks with fully labeled data.
- D) A model that learns without any data.
- Which method is used to fine-tune a model for a specific task after pre-training on large datasets?
- A) Reinforcement Learning.
- B) Fine-tuning.
- C) Transfer Learning.
- D) Data Augmentation.
- What does the F1 Score balance in a classification task?
- A) Precision and Recall.
- B) Accuracy and Precision.
- C) Recall and Specificity.
- D) Sensitivity and Specificity.
- What is a common technique used to avoid overfitting in neural networks?
- A) Gradient Descent.
- B) Regularization (e.g., L2 weight decay).
- C) Data Normalization.
- D) Data Augmentation.
True/False Questions:
- A Generative Model learns to classify data into predefined categories.
- True
- False
- In a Recurrent Neural Network (RNN), the model’s hidden state is passed through time steps.
- True
- False
- Dropout is a technique used to encourage overfitting.
- True
- False
- Attention Heads in Transformers allow the model to process input in parallel, improving representational power.
- True
- False
- Self-Attention mechanisms allow a model to consider the entire sequence when making predictions.
- True
- False
Short Answer Questions:
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Explain what “Reinforcement Learning” is in one sentence.
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Describe the difference between a “Generative” and a “Discriminative” model.
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What is the purpose of using “Transfer Learning” in machine learning?
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Why is “Cross-validation” important in evaluating model performance?
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What is the difference between “Batch Normalization” and “Dropout” in neural networks?
This quiz covers key concepts from the provided content!
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Additional Multiple Choice Questions (MCQ):
- Which of the following is a key feature of the GPT model?
- A) It uses recurrent layers for text generation.
- B) It is trained on sequential data using supervised learning.
- C) It uses the Transformer architecture and generates human-like text.
- D) It uses convolutional layers to process input data.
- Which of the following learning methods allows a model to improve based on feedback from human preferences?
- A) Reinforcement Learning from Human Feedback (RLHF)
- B) Supervised Learning
- C) Unsupervised Learning
- D) Self-Supervised Learning
- What does “Tokenization” refer to in NLP (Natural Language Processing)?
- A) Splitting text into grammatical parts.
- B) Converting text into a fixed-size vector.
- C) Splitting text into individual words or subwords that the model processes.
- D) Encrypting sensitive text data.
- What is the primary purpose of “Data Augmentation”?
- A) To reduce the amount of data used for training.
- B) To artificially expand the training dataset and improve robustness.
- C) To improve model inference speed.
- D) To clean the dataset from outliers.
- Which of the following is NOT a type of neural network used for sequential data?
- A) Recurrent Neural Network (RNN)
- B) Long Short-Term Memory (LSTM)
- C) Convolutional Neural Network (CNN)
- D) Gated Recurrent Unit (GRU)
- What is the role of the “Learning Rate” hyperparameter in training a neural network?
- A) It determines how fast the model can predict new outputs.
- B) It controls how large the model’s architecture is.
- C) It affects how drastically the weights are updated during training.
- D) It influences how many epochs are needed to train the model.
- Which of the following is a regularization technique to reduce overfitting by randomly dropping out neurons during training?
- A) Batch Normalization
- B) Dropout
- C) Stochastic Gradient Descent
- D) Weight Decay
- In a “Confusion Matrix,” what does the term “True Positive” represent?
- A) The number of correctly predicted negative examples.
- B) The number of correctly predicted positive examples.
- C) The number of incorrectly predicted negative examples.
- D) The number of incorrectly predicted positive examples.
- Which of the following is a measure of how well a model predicts the next token in a sequence?
- A) Precision
- B) Perplexity
- C) Accuracy
- D) Recall
- Which technique in Machine Learning aims to combine multiple models to improve performance and stability?
- A) Ensemble Methods
- B) Transfer Learning
- C) Data Preprocessing
- D) Data Augmentation
Additional True/False Questions:
- Generative Adversarial Networks (GANs) consist of a generator and a discriminator that compete with each other.
- True
- False
- In “Causal Language Modeling,” the model predicts the next token based on all previous tokens in a sequence.
- True
- False
- Precision is the ratio of true positive predictions to all predictions, including false positives.
- True
- False
- Batch Normalization helps in stabilizing the training of deep networks by normalizing layer outputs.
- True
- False
- The purpose of “Positional Encoding” in Transformers is to prevent the model from learning any sequence information.
- True
- False
- Residual connections in neural networks help mitigate the vanishing gradient problem by allowing the gradient to flow more easily through the network.
- True
- False
- Model Overfitting occurs when the model fails to perform well on training data but works well on new data.
- True
- False
- Federated Learning trains models across decentralized devices without exchanging local data.
- True
- False
Additional Short Answer Questions:
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What is the difference between “Supervised” and “Unsupervised” learning?
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What is the purpose of the “Context Window” in a Transformer model?
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How does “Transfer Learning” benefit a model when applied to a new task?
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In “Reinforcement Learning,” what role does a reward function play in training the agent?
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Explain how the “Attention Mechanism” helps improve the performance of language models like GPT.
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What is the difference between “Encoder-Decoder” architecture and “Causal Language Modeling” in Transformers?
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What is the function of “Vector Embeddings” in NLP tasks?
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What is the significance of the “F1 Score” when evaluating imbalanced datasets?
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What does “Explainable AI (XAI)” aim to achieve in Machine Learning models?
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What is the advantage of using “Few-shot Learning” for a task with limited data?
These additional questions will help further assess the user’s understanding of machine learning, deep learning, and GPT models!