100 Facts About Convolutional Neural Networks (CNNs)

Photo by Pawel Czerwinski on Unsplash

Here’s a list of 100 technical facts about Convolutional Neural Networks (CNNs):

1. Definition: Convolutional Neural Networks (CNNs) are a class of deep neural networks designed for image recognition and processing.
2. Inspiration: CNNs are inspired by the visual processing in the human brain, with a focus on hierarchical feature learning.
3. Local Connectivity: CNNs leverage local connectivity, where neurons respond to a subset of the input space.
4. Convolutional Layers: CNNs use convolutional layers to detect patterns or features in input data.
5. Convolution Operation: The convolution operation involves sliding a filter (kernel) over the input data to extract features.
6. Stride: Stride in convolution defines the step size when sliding the filter over the input.
7. Padding: Padding is added to the input to preserve spatial dimensions during convolution.
8. Pooling Layers: Pooling layers reduce spatial dimensions, helping to decrease computational complexity.
9. Max Pooling: A pooling operation that retains the maximum value from a group of values in the input.
10. Average Pooling: A pooling operation that calculates the average value from a group of…