Image classification demo
Import Libraries and Dataset (e.g., MNIST or CIFAR-10)
Preprocess the Data (Normalization, One-Hot Encoding, etc.)
Build the CNN Model (Convolution, Pooling, Flatten, Dense layers)
Compile the Model (Optimizer, Loss, Metrics)
Train the Model on the Dataset
Evaluate the Model (Accuracy & Loss on Test Data)
Explain How CNN Layers Work (Convolution, Pooling, Feature Extraction, Classification)
Summarize Results and Key Insights
