Convolutional Neural Network

In deep learning, a convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. They are also known as shift invariant or space invariant artificial neural networks, based on their shared-weights architecture and translation invariance characteristics. kindly refthe blog given below

https://towardsdatascience.com/convolutional-neural-network-17fb77e76c05

Convolution is the process of convolving a kernel with an image (sum of multiplication of corresponding pixels in the kernel and image). The kernel operates over the whole image. You can find lots of information online regarding this technique if I wasn’t descriptive enough.

A convolutional neural network is a neural network, where the input is an image and convolutions with various kernels occur in each layer to produce the convolved image as the input into the next layers. There are often other types of layers in better convolutional layers such as pooling layers that extract the important information/features from the previous layer’s convolutions.