Deep Learning - Convolutional Neural Networks with TensorFlow - Data Augmentation

The video tutorial discusses data augmentation, a technique used to improve machine learning models by generating new data from existing images. It highlights the challenges of data storage and the benefits of using Python generators to create data on the fly. The tutorial explains how to implement data augmentation in Keras using the ImageDataGenerator class, which allows for various transformations like rotation, shifting, and flipping. The use of generators helps in efficient memory usage and enhances model generalization.

What is the primary benefit of data augmentation in deep learning?

• It simplifies the model architecture.

• It increases the diversity of the training dataset.

• It reduces the need for labeled data.

• It speeds up the training process.

Why is it not feasible to store all possible augmented images?

• It would decrease the model's accuracy.

• It would take up excessive storage space.

• It would introduce bias into the dataset.

• It would require too much computational power.

How does Python's range function differ in Python 3 compared to Python 2?

• It requires more memory in Python 3.

• It is not available in Python 3.

• It generates numbers on the fly in Python 3.

• It returns a list in Python 3.

What is the role of the yield command in Python generators?

• It pre-calculates all values before execution.

• It stores all values in memory.

• It increases the speed of the loop.

• It generates values on the fly during iteration.

Which Keras function is used to handle data augmentation during model training?

• evaluate

• fit

• compile

• fit_generator

What is the purpose of the ImageDataGenerator in Keras?

• To reduce the size of the dataset.

• To apply real-time data augmentation during training.

• To store augmented images in memory.

• To increase the model's complexity.

Why is it important to set the correct range of values when creating a generator object in Keras?

• To increase the batch size.

• To reduce the number of epochs needed.

• To avoid generating invalid data.

• To ensure the model trains faster.