Convolutional Neural Networks (CNNs) have been a popular choice for tasks such as image classification, object detection, and natural language processing. They have achieved state-of-the-art performance on a variety of tasks due to their ability to learn powerful features from data. However, one limitation of CNNs is that they may not always be able to capture long-range dependencies or relationships in the data. This is where attention mechanisms come into play. Attention mechanisms allow a model to focus on specific parts of the input when processing it, rather than processing the entire input equally. This can be especially useful for tasks such as machine translation, where the model needs to pay attention to different parts of the input at different times. In this tutorial, we will learn how to implement a CNN with an attention layer in Keras and TensorFlow. We will use a dataset of images of clothing items and train the model to classify them into different categories. Setting