All about Convolutional Neural Networks (CNNs)

Savindi Wijenayaka
16 min readJun 6, 2021

Hola Readers!

Today I come to you with yet another interesting topic in Deep Learning; Convolutional Neural Networks (CNN). Even though this topic should ideally come after discussing lots of other Machine learning and Deep Learning theories, I decided to go ahead and write this article. However, I tried my best to introduce all the terms I used and also to explain things in detail so that you can understand everything even without previous knowledge in the field.

Today’s discussion outline is as follows;

  1. What is CNN?
  2. What we can do with image data?
  3. Convolutional Layer and Feature Detectors
  4. Padding and Dimensions
  5. Pooling Layer
  6. Flatten Layer
  7. Fully Connected Layer
  8. Convolutions on RGB images
  9. Summary of Notations and Equations
  10. Transfer Learning
  11. Why CNN and not ANN?

Without further ado, let's dive right in. (I have a feeling this will be a bit longer post but I guarantee I will keep it interesting.)

1. What is CNN?

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Savindi Wijenayaka

Ph.D candidate at University of Auckland. Software Engineer passionate about using Machine learning to revolutionize healthcare.