A. The steps involved in a Convolutional Neural Network (CNN) can be summarized as follows: 1. Convolution: Apply convolutional filters to input data to extract local features. 2. Activation: Introduce non-linearity by applying an activation function (e.g., ReLU) to the convolved features. 3. Pooling: Downsample the convolved features using pooling operations (e.g., max pooling) to reduce spatial dimensions and extract dominant features. 4. Flattening: Convert the pooled features into a one-dimensional vector to prepare for input into fully connected layers. 5. Fully Connected Layers: Connect the flattened features to traditional neural network layers to learn high-level representations and make predictions. 6. Output Layer: The final layer produces the network's output, often using a suitable activation function based on the problem (e.g., softmax for classification). These steps collectively allow CNNs to effectively learn hierarchical representations from input data, making them particularly suited for tasks such as image classification, object detection, and computer vision applications.