Breaking the problem down into features

Given the CIFAR-10 dataset and the application of CNNs for image recognition, we outline the following features to guide users through building and optimizing CNN models:

• Building the baseline CNN model with a single convolutional layer: Users will start by constructing a simple CNN model with a single convolutional layer for image classification. This feature focuses on defining the basic architecture, including convolutional filters, activation functions, and pooling layers, to establish a foundational understanding of CNNs.
• Experimenting with the addition of convolutional layers: Users will explore the impact of adding additional convolutional layers to the baseline model architecture. By incrementally increasing the depth of the network, users can observe how the model’s capacity to capture hierarchical features evolves and its ability to learn complex patterns improves.
• Incorporating dropout regularization...