“Hey, Kernel. You work for Mr. Convolution, right? What do you do there?” The pixelated giant asked, to which the young Kernel response, “A convolution is a mathematical operation that blends two functions to produce a third. In image processing, you slide a filter (kernel) over an image.”
“So, my job is sliding. It’s fun! At each position, you compute a dot product between the filter and the image patch. The result is a feature map that highlights patterns like edges, textures, or shapes.” He added
“Wanna see a performance?” Kernel asked. The giant said yes.
“Like this. At each position, you compute a dot product between the filter and the image patch.”
“The result is a feature map that highlights patterns like edges, textures, or shapes.” Kernel explained.
“Yes, this reduces the number of parameters compared to fully connected layers. It captures spatial hierarchies — local patterns first, then global ones if you have many convolution layers in your network.” A lady kernel said.
“Hi hi, and a Convolutional Neural Network (CNN) is a type of deep neural network designed to process grid-like data (like images). It uses convolutional operations to extract features and learn patterns.”
“And all the glowing Feature Maps—the straight lines, the curves, the spots—then gathered together. They swirled and condensed, forming a new, smaller, and much clearer version of the giant. This was Pooled Image, a summary of the most important parts.”
“So cool!” The gaint said. Then Mr. Owl added “The process began again, but faster this time. More Kernels, with even more complex patterns, zipped and zoomed across the surface of Pooled Image, pulling out finer and finer details, creating layer upon layer of intricate, glowing maps.”
“In the end, the fully connected layer will connect (combine) all features together for the final classification, for example.” The giant said, to which the little girl added “Yes, and the classification is thanks to the last softmax layer.”
🧱 Key Components of CNNs:
| Layer Type | Purpose |
|---|---|
| Convolutional Layer | Extracts features using filters |
| Activation (ReLU) | Adds non-linearity to model complex patterns |
| Pooling Layer | Downsamples feature maps to reduce computation and retain key features |
| Fully Connected Layer | Combines features for final classification |
| Softmax Layer | Outputs probabilities for each class |
🧬 CNN Workflow
- Input Image (e.g., 32×32×3 RGB)
- Convolution → Feature maps
- ReLU Activation
- Pooling → Smaller feature maps
- Repeat steps 2–4
- Flatten → 1D vector
- Fully Connected Layers
- Softmax Output → Class prediction