Avatto>>DATA SCIENTIST>>SHORT QUESTIONS>>Deep Learning>>Convolution Neural Networks
The convolutional neural network uses the following three important layers:
Convolutional layer
Pooling layer
Fully connected layer
We take the input matrix and one more matrix called the filter matrix. We slide the filter matrix over the input matrix by n number of pixels, perform element-wise multiplication, sum up the results, and produce a single number and this operation is known as convolution.
The matrix obtained as a result of convolution operation is often called activation maps or feature maps.
In the convolution operation, we take the filter matrix and slide it over the input matrix by n number of pixels, perform element-wise multiplication, sum the result, and produce a single number. The number of pixels we choose to slide the filter matrix over the input matrix is often called the stride.
If we set stride to a high value, then it takes us less time to compute but we might miss out on some important feature from the image. If we set stride to a low value, then we can learn the more detailed representation fo the image but it will take us a lot of time to compute.


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