Deep Learning and AI SuperheroChapter 43

3. Deep Learning with Keras (Chapter 3)

Section 3 of 4-~ 12 min read-Synced from Cuantum content
  1. What is the main difference between the Sequential API and the Functional API in Keras?
  2. - a) The Sequential API is used for building complex models, while the Functional API is only for simple models.
  • b) The Sequential API allows for more complex architectures, such as multi-input/output models, while the Functional API is limited to simple linear models.
  • c) The Sequential API is used for building simple, linear stacks of layers, while the Functional API allows for more complex architectures like multiple inputs/outputs and shared layers.
  • d) The Sequential API is used for transfer learning, and the Functional API is for training models from scratch.
  1. What is the purpose of the ModelCheckpoint callback in Keras?
  2. - a) To monitor model performance and stop training when it starts to overfit.
  • b) To save the model’s weights or the entire model during training, often when the performance improves.
  • c) To log the learning rate and other hyperparameters during training.
  • d) To train the model with multiple datasets in parallel.
  1. How does EarlyStopping prevent overfitting in Keras models?
  2. - a) By reducing the learning rate automatically during training.
  • b) By stopping the training process once the model’s performance on the validation set ceases to improve.
  • c) By saving the best-performing model during training.
  • d) By skipping validation steps to increase training speed.
  1. When deploying a Keras model using Flask, what is the typical purpose of the Flask framework?
  2. - a) To scale machine learning models for distributed training.
  • b) To build a lightweight web application that serves predictions via a RESTful API.
  • c) To optimize model performance in mobile applications.
  • d) To perform hyperparameter tuning during training.
  1. What is the primary purpose of converting a Keras model to TensorFlow Lite format?
  2. - a) To train the model faster using GPUs.
  • b) To enable the model to run efficiently on mobile or embedded devices.
  • c) To improve the accuracy of the model on large datasets.
  • d) To reduce the time needed for training the model on cloud infrastructure.