Evaluate the impact of an AI application used in the real world. (case study) : Working with Flower Images: Case Study -

The video tutorial explains the need for a custom data generator instead of using Karas due to specific customization requirements. It covers the initialization method, focusing on the importance of mode for training, validation, and testing. The tutorial discusses batch processing, dimensions, and shuffling to optimize memory usage. It also explains handling data paths, list IDs, and labels, and concludes with details on the data generation method, emphasizing the benefits of customization for better results.

Why is it necessary to create a custom data generator instead of using the Keras data generator?

• To avoid using any pre-built libraries

• To allow for customization and optimization

• To make the code more complex

• To reduce the amount of data used

What is the purpose of the 'mode' parameter in the data generator?

• To determine the type of data transformation

• To specify the batch size

• To set the image dimensions

• To differentiate between training, validation, and testing

Why is batch processing used in data generators?

• To process images one by one

• To avoid using RGB format

• To increase the complexity of the code

• To handle large datasets without memory issues

What is the significance of shuffling data in batches?

• To prevent learning any specific pattern

• To ensure all zeros are processed first

• To reduce the number of epochs

• To increase processing speed

How is the data split between training and testing?

• 80% training and 20% testing

• 90% training and 10% testing

• 70% training and 30% testing

• 50% training and 50% testing

What does the data generation method return?

• A single image

• The entire dataset

• A batch of images and their labels

• Only the labels

Why is it beneficial to experiment with custom data generators?

• To avoid using any pre-built functions

• To better understand the data processing

• To make the code more complex

• To reduce the dataset size