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Menu iconMenu iconDeep Learning and AI Superhero
Deep Learning and AI Superhero
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Quiz Part 1: Neural Networks and Deep Learning Basics
1. Introduction to Neural Networks and Deep Learning (Chapter 1)
2. Deep Learning with TensorFlow 2.x (Chapter 2)
3. Deep Learning with Keras (Chapter 3)
Answers to the Quiz
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Quiz Part 1: Neural Networks and Deep Learning Basics

2. Deep Learning with TensorFlow 2.x (Chapter 2)

  1. What is eager execution in TensorFlow 2.x?
    • a) A mode where TensorFlow operations are executed lazily to optimize performance.
    • b) A mode where TensorFlow operations are executed immediately, making it easier to debug and develop models interactively.
    • c) A special mode for GPU acceleration in TensorFlow.
    • d) A mode where operations are executed only after calling the tf.run() function.
  2. In TensorFlow 2.x, which API is typically used for building deep learning models?
    • a) Estimator API
    • b) Dataset API
    • c) Keras API
    • d) Sequential API
  3. Which of the following is NOT a common use case for pretrained models from TensorFlow Hub?
    • a) Image classification
    • b) Object detection
    • c) Reinforcement learning
    • d) Text embedding
  4. What is the primary advantage of using TensorFlow Serving in production?
    • a) It allows deploying machine learning models as scalable web services.
    • b) It is a tool for optimizing hyperparameters during model training.
    • c) It is used for monitoring the performance of models during training.
    • d) It improves the accuracy of models during training by fine-tuning.

2. Deep Learning with TensorFlow 2.x (Chapter 2)

  1. What is eager execution in TensorFlow 2.x?
    • a) A mode where TensorFlow operations are executed lazily to optimize performance.
    • b) A mode where TensorFlow operations are executed immediately, making it easier to debug and develop models interactively.
    • c) A special mode for GPU acceleration in TensorFlow.
    • d) A mode where operations are executed only after calling the tf.run() function.
  2. In TensorFlow 2.x, which API is typically used for building deep learning models?
    • a) Estimator API
    • b) Dataset API
    • c) Keras API
    • d) Sequential API
  3. Which of the following is NOT a common use case for pretrained models from TensorFlow Hub?
    • a) Image classification
    • b) Object detection
    • c) Reinforcement learning
    • d) Text embedding
  4. What is the primary advantage of using TensorFlow Serving in production?
    • a) It allows deploying machine learning models as scalable web services.
    • b) It is a tool for optimizing hyperparameters during model training.
    • c) It is used for monitoring the performance of models during training.
    • d) It improves the accuracy of models during training by fine-tuning.

2. Deep Learning with TensorFlow 2.x (Chapter 2)

  1. What is eager execution in TensorFlow 2.x?
    • a) A mode where TensorFlow operations are executed lazily to optimize performance.
    • b) A mode where TensorFlow operations are executed immediately, making it easier to debug and develop models interactively.
    • c) A special mode for GPU acceleration in TensorFlow.
    • d) A mode where operations are executed only after calling the tf.run() function.
  2. In TensorFlow 2.x, which API is typically used for building deep learning models?
    • a) Estimator API
    • b) Dataset API
    • c) Keras API
    • d) Sequential API
  3. Which of the following is NOT a common use case for pretrained models from TensorFlow Hub?
    • a) Image classification
    • b) Object detection
    • c) Reinforcement learning
    • d) Text embedding
  4. What is the primary advantage of using TensorFlow Serving in production?
    • a) It allows deploying machine learning models as scalable web services.
    • b) It is a tool for optimizing hyperparameters during model training.
    • c) It is used for monitoring the performance of models during training.
    • d) It improves the accuracy of models during training by fine-tuning.

2. Deep Learning with TensorFlow 2.x (Chapter 2)

  1. What is eager execution in TensorFlow 2.x?
    • a) A mode where TensorFlow operations are executed lazily to optimize performance.
    • b) A mode where TensorFlow operations are executed immediately, making it easier to debug and develop models interactively.
    • c) A special mode for GPU acceleration in TensorFlow.
    • d) A mode where operations are executed only after calling the tf.run() function.
  2. In TensorFlow 2.x, which API is typically used for building deep learning models?
    • a) Estimator API
    • b) Dataset API
    • c) Keras API
    • d) Sequential API
  3. Which of the following is NOT a common use case for pretrained models from TensorFlow Hub?
    • a) Image classification
    • b) Object detection
    • c) Reinforcement learning
    • d) Text embedding
  4. What is the primary advantage of using TensorFlow Serving in production?
    • a) It allows deploying machine learning models as scalable web services.
    • b) It is a tool for optimizing hyperparameters during model training.
    • c) It is used for monitoring the performance of models during training.
    • d) It improves the accuracy of models during training by fine-tuning.
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