Convolutional Neural Networks(CNN) is one of the popular Deep Artificial Neural Networks. CNN’s are made up of learnable weights and biases. CNN’s are very similar to ordinary neural networks but not exactly the same.

CNN’s are primarily used in image recognition, image clustering, and classification, object detection, etc…

Why CNN’s?

CNN’s is weight sharing, less complex, and occupies less memory.

Let’s take an MNIST data set image, and it’s passed to CNN and NN.

Assume on the CNN layer,10 filters of 5x5 size, then we have 5x5x10 +10(biases) =260 params.

Assume the image dimensions 784, and a NN layer of 250 neurons, then in Neural Network (NN) we have 784 x 260 + 1= 19601 params

So, CNN’s outperform NNs on conventional image recognition tasks and many other tasks.

The idea behind the working of CNN

convolution operation in computer vision is biologically inspired by the brain’s visual cortex. The connectivity pattern of CNN resembles the structure of the animal visual cortex.

If an image is passed to the visual cortex, then the cortex processes that information through the segments/layers. The brain extracts information from every segment/layer. The first layers learn representations such as edges or color while the intermediate-level layers learn intermediate abstract representations such as object parts and finally, high-level layers learn full objects like cat’s faces. with an increase in the levels of abstractions, inferences become more clear. Thus, the brain makes decisions from the information it has learned through all layers.

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