Designing, CNN architecture, neural network, deep learning, convolutional layers, pooling layers, fully connected layers, ReLU activation, batch normalization, dropout regularization, input image, 3x224x224, RGB, classification task, 10 classes, training dataset, validation dataset, testing dataset, accuracy metric, loss function, optimizer algorithm, Adam optimizer, learning rate, decay rate, epochs, batch size, GPU acceleration, TensorFlow framework, Keras API, Python implementation, clean code, modular design, architecture diagram, layer visualization, neural network visualization, 3D visualization.