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Machine Learning and Deep Learning Quiz

Multiple Choice Questions (MCQ):

  1. 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.
  2. 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.
  3. 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.
  4. What does the Transformer architecture primarily rely on for natural language processing?
    • A) Convolution layers.
    • B) Recurrent layers.
    • C) Attention mechanisms.
    • D) Decision trees.
  5. 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.
  6. 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).
  7. 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.
  8. 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.
  9. 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.
  10. 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:

  1. A Generative Model learns to classify data into predefined categories.
    • True
    • False
  2. In a Recurrent Neural Network (RNN), the model’s hidden state is passed through time steps.
    • True
    • False
  3. Dropout is a technique used to encourage overfitting.
    • True
    • False
  4. Attention Heads in Transformers allow the model to process input in parallel, improving representational power.
    • True
    • False
  5. Self-Attention mechanisms allow a model to consider the entire sequence when making predictions.
    • True
    • False

Short Answer Questions:

  1. Explain what “Reinforcement Learning” is in one sentence.

  2. Describe the difference between a “Generative” and a “Discriminative” model.

  3. What is the purpose of using “Transfer Learning” in machine learning?

  4. Why is “Cross-validation” important in evaluating model performance?

  5. 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):

  1. 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.
  2. 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
  3. 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.
  4. 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.
  5. 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)
  6. 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.
  7. 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
  8. 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.
  9. 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
  10. 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:

  1. Generative Adversarial Networks (GANs) consist of a generator and a discriminator that compete with each other.
    • True
    • False
  2. In “Causal Language Modeling,” the model predicts the next token based on all previous tokens in a sequence.
    • True
    • False
  3. Precision is the ratio of true positive predictions to all predictions, including false positives.
    • True
    • False
  4. Batch Normalization helps in stabilizing the training of deep networks by normalizing layer outputs.
    • True
    • False
  5. The purpose of “Positional Encoding” in Transformers is to prevent the model from learning any sequence information.
    • True
    • False
  6. Residual connections in neural networks help mitigate the vanishing gradient problem by allowing the gradient to flow more easily through the network.
    • True
    • False
  7. Model Overfitting occurs when the model fails to perform well on training data but works well on new data.
    • True
    • False
  8. Federated Learning trains models across decentralized devices without exchanging local data.
    • True
    • False

Additional Short Answer Questions:

  1. What is the difference between “Supervised” and “Unsupervised” learning?

  2. What is the purpose of the “Context Window” in a Transformer model?

  3. How does “Transfer Learning” benefit a model when applied to a new task?

  4. In “Reinforcement Learning,” what role does a reward function play in training the agent?

  5. Explain how the “Attention Mechanism” helps improve the performance of language models like GPT.

  6. What is the difference between “Encoder-Decoder” architecture and “Causal Language Modeling” in Transformers?

  7. What is the function of “Vector Embeddings” in NLP tasks?

  8. What is the significance of the “F1 Score” when evaluating imbalanced datasets?

  9. What does “Explainable AI (XAI)” aim to achieve in Machine Learning models?

  10. 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!

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