Convolutional Neural Network: How is it different from the other networks?

Convolutional Neural Network: How is it different from the other networks? What’s so unique about CNNs and what does convolution really do? This is a math-free introduction to the wonders of CNNs.

I am not a deep learning researcher, but I’ve come to know a few things about neural networks through various exposures. I’ve always heard that CNN is a type of neural network that’s particularly good at image-related problems. But, what does that really mean? What’s with the word “convolutional”? What’s so unusual about an image-related problem that a different network is required?

Recently I had the opportunity to work on a COVID-19 image classification problem and built a CNN-based classifier using tensorflow.keras that achieved an 85% accuracy rate. Finally, I think I’ve figured out the answers to those questions. Let me share with you those answers in a math-free way. If you are already familiar with CNNs, this post should feel like a good refresher. If not, take a look, you could gain an intuitive understanding of the motivation behind CNNs and the unique features that define a CNN.

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