In this hands-on practice session, we will focus on building, training, and evaluating a Convolutional Neural Network (CNN) for image classification. CNNs are a class of deep learning models widely used for analyzing visual data. They are designed to automatically and adaptively learn spatial hierarchies of features from input images, making them highly effective for tasks such as image recognition, object detection, and segmentation.

Our objective is to apply a CNN model to classify a dataset of images into three categories: COVID, normal, and virus. The dataset consists of labeled images stored in separate directories, and we will use TensorFlow and Keras libraries to implement the CNN. The process involves several steps, including data preprocessing, model building, training, evaluation, and generating predictions.