👋 CNN get easier to understand if you consider how we solve word puzzles (An illustrative thread) 👇🏾
#100DaysOfMLCode
#100DaysOfMLCode
Neural Networks came back into limelight when Prof. Yan Lecun introduced a mathematical operation, called convolution, in one of the layers of a digit recognition model. For NNs, it just turned the world upside down
Most of the AI Applications that we see today are based on convolutional neural networks (CNN).
Convolution op or use of kernels is at the center of CNNs but can be daunting to understand at first. But word puzzles offer an easy way to think about them.
In word puzzles like this, you are expected to find words. The word could are either on horizontal, vertical or cross lines. Go ahead try to find words in this one
The first thing I usually do is look for vowels scanning through each block
Once I have vowels, I look for adjacent blocks. This helps finding 2-letter words. This time we are looking 2x2 blocks at a time
No luck with 2-letter words. We move search to 3x3 blocks. Now are looking for neighbours' of neighbour (of vowels we found in step 1)
There they are. We found 2 words.
The 2x2 and 3x3 block perimeter - that we set for our search - is how kernel works in convolution operation.
In summary,
- Kernels are short filters to look for a specific pattern at each step
- The initial layers identify high level features while deeper layers find features more closer to the actual object
- Kernels are short filters to look for a specific pattern at each step
- The initial layers identify high level features while deeper layers find features more closer to the actual object
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