A convolution operation operates on pixel values within a kernel's receptive field, multiplying kernel weights with pixel values and adding a bias term to produce a single output value. This reduces the input matrix dimensions. Convolutional neural networks share weights across neurons in a feature map to reduce parameters and computation. CNNs are equivariant to translations, so translating the input results in translating the output. Padding preserves the spatial dimensions after convolution by adding pixels around the input border. The stride specifies the number of pixels to move the kernel each time, controlling the resolution of outputs. RELU is used to decrease non-linearity in images for easier prediction.

1. CONVOLUTION OPERATION
A convolution operation operates on all the pixel
values within its kernel's receptive field,
producing a single value by essentially multiplying
the kernel weights with the pixel values
elementwise and adding a bias term to the result.
This reduces the dimensions of the input matrix
as well.

3. parameter sharing
• Convolution Neural Networks have a couple of
techniques known as parameter sharing and
parameter tying. Parameter sharing is the
method of sharing weights by all neurons in a
particular feature map. Therefore helps to
reduce the number of parameters in the
whole system, making it computationally
cheap.

5. EQUIVARIANT REPRESENTATION
• CNNs are famously equivariant with respect to
translation. This means that translating the
input to a convolutional layer will result in
translating the output

7. convolution operation padding
• padding is a technique used to preserve the
spatial dimensions of the input image after
convolution operations on a feature map.
Padding involves adding extra pixels around
the border of the input feature map before
convolution

9. STRIDE
• The number of pixels turning to the input
matrix is known as the strides. When the
number of strides is 1, we move the filters to 1
pixel at a time. Similarly, when the number of
strides is 2, we carry the filters to 2 pixels, and
so on.

10. RELU
• Usually, the image is highly non-linear, which
means varied pixel values. This is a scenario
that is very difficult for an algorithm to make
correct predictions. RELU activation function is
applied in these cases to decrease the non-
linearity and make the job easier