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

As an AI, I need the options to answer your question. However, typically in a Convolutional Neural Network (ConvNet/CNN), you would see:

1. Convolutional Layers: These are the major building blocks of ConvNets. They perform a dot product between their weights and a small region they are connected to in the input volume.

2. Pooling Layers: These are commonly placed between successive convolutional layers in a ConvNet architecture. Their function is to progressively reduce the spatial size of the representation, to reduce the amount of parameters and computation in the network.

3. Fully Connected Layers: Neurons in a fully connected layer have connections to all activations in the previous layer. This layer computes class scores, resulting in volume size [1x1x10], where each of the 10 numbers correspond to a class score.

4. ReLU Layers: This layer applies an elementwise activation function, such as the max(0,x) thresholding at zero. This leaves the size of the volume unchanged.

5. Dropout Layers: Dropout is a regularization technique for reducing overfitting in neural networks. It is a very efficient way of performing model averaging with neural networks.

6. Batch Normalization Layers: This layer is used to normalize the inputs of each layer, in such a way to speed up the training of the network.

Remember, the specific architecture of a ConvNet can vary from one to another, but these are the typical layers you would see.