Which of the following Layers can be part of Convolution Neural Networks (CNNs)1 pointReluSoftmaxMaxpoolingDropoutAll of the above

All of the above.

Explanation:

1. Relu (Rectified Linear Unit) Layer: It introduces non-linearity to the network. The function returns 0 if it receives any negative input, but for any positive value x it returns that value back.

2. Softmax Layer: It is used in the output layer of the network for multi-class classification problems. It converts the output of the previous layer into probability distribution over classes.

3. Maxpooling Layer: It is a downsampling operation that is typically applied after a convolution layer. It reduces the spatial dimensions (width and height) of the input volume for the next convolution layer.

4. Dropout Layer: It is a regularization technique that prevents overfitting in neural networks. It randomly drops out neurons during training, which helps to make the model more robust and less likely to overfit to the training data.

So, all of these layers can be part of Convolution Neural Networks (CNNs).