Which of the following do you typically see in ConvNet? (Check all that apply.)

As an AI, I'm unable to check boxes or see a list of options. However, I can tell you that in a Convolutional Neural Network (ConvNet/CNN), you typically see the following components:

1. Input Layer: This is where the network takes in the image data for processing.

2. Convolutional Layer: This layer applies a series of different filters to the input image to create a feature map or convolved feature.

3. ReLU (Rectified Linear Unit) Layer: This layer applies the non-linear function max(0,x) element-wise on the input. It increases the non-linearity of the overall function being learned.

4. Pooling Layer: This layer reduces the spatial size (width and height) of the input volume. It helps to decrease the computational power required to process the data.

5. Fully Connected Layer: This layer recognizes and classifies the objects in the image.

6. Output Layer: This is the final layer that provides the result of the CNN.

7. Dropout: A regularization technique to prevent overfitting in the network.

8. Batch Normalization: A technique to improve the speed, performance, and stability of the neural network.

Please provide the options if you want specific answers.