What does shift and multiply-accumulate mean in terms of Convolutional Neural Networks?

1. What is stride and padding in CNN?
2. How feature extraction is done in CNN?
3. Why do we use padding in CNN?
4. How does convolution work in CNN?

What is stride and padding in CNN?

Stride denotes how many steps we are moving in each steps in convolution.By default it is one. Convolution with Stride 1. We can observe that the size of output is smaller that input. To maintain the dimension of output as in input , we use padding. Padding is a process of adding zeros to the input matrix symmetrically ...

How feature extraction is done in CNN?

CNN's output layer typically uses the neural network for multiclass classification. CNN uses the feature extractor in the training process instead of manually implementing it. CNN's feature extractor consists of special types of neural networks that decide the weights through the training process.

Why do we use padding in CNN?

Padding is simply a process of adding layers of zeros to our input images so as to avoid the problems mentioned above. This prevents shrinking as, if p = number of layers of zeros added to the border of the image, then our (n x n) image becomes (n + 2p) x (n + 2p) image after padding.

How does convolution work in CNN?

Convolution is a mathematical operation that allows the merging of two sets of information. In the case of CNN, convolution is applied to the input data to filter the information and produce a feature map. This filter is also called a kernel, or feature detector, and its dimensions can be, for example, 3x3.