Labeling Image Data Using Data Augmentation
In this chapter, we will learn how to label image data using data augmentation for semi-supervised machine learning. We will use the CIFAR-10 dataset and the MNIST dataset of handwritten digits to generate labels using data augmentation. From there we will build an image classification machine learning model.
Data augmentation plays a crucial role in data labeling by enhancing the diversity, size, and quality of the dataset. Data augmentation techniques generate additional samples by applying transformations to existing data. This effectively increases the size of the dataset, providing more examples for training and improving the model’s ability to generalize.
In this chapter, we will cover the following:
- How to prepare training data with image data augmentation and implement support vector machines
- How to implement convolutional neural networks with augmented image data
