How to apply neural network parallelly on each part of image

1. Why do we use pooling layer in CNN?
2. Why CNN is better than MLP for image classification?
3. How many images can a neural network classify?

Why do we use pooling layer in CNN?

Why to use Pooling Layers? Pooling layers are used to reduce the dimensions of the feature maps. Thus, it reduces the number of parameters to learn and the amount of computation performed in the network. The pooling layer summarises the features present in a region of the feature map generated by a convolution layer.

Why CNN is better than MLP for image classification?

Both MLP and CNN can be used for Image classification however MLP takes vector as input and CNN takes tensor as input so CNN can understand spatial relation(relation between nearby pixels of image)between pixels of images better thus for complicated images CNN will perform better than MLP.

How many images can a neural network classify?

The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes, with 6000 images per class.