An interactive exploration of images and filters in real time.
Or does it merely compute patterns in a grid of numbers?
You open your eyes and see the world — instantly, effortlessly, without conscious thought. To a machine, an image is a grid of numbers. Each pixel is a value from 0 to 255. Finding a "cat" in that array of numbers — that is the mathematics of Convolutional Neural Networks.
00 — WHAT A MACHINE SEES
When you see a photo, your brain interprets patterns of light. When a machine sees the same photo, all it sees is a matrix of numbers — each cell a brightness value from 0 (black) to 255 (white).
The meaning of "seeing" is entirely different. — Human vision grasps meaning intuitively. To a machine, everything is a 2D array of numbers.
01 — THE CORE OPERATION
A small filter (kernel) slides over the image. At each position, it computes the sum of element-wise products between the filter and the image patch.
Convolution is the 2D extension of the dot product. — Just as the dot product in Part II measured similarity between two vectors, convolution measures similarity between an image patch and a filter template.
02 — THE MACHINE'S VISUAL CORTEX
First layers find edges, next layers textures, deeper layers object parts, final layers whole concepts. This hierarchy is strikingly similar to the human visual cortex (V1→V2→V4→IT).
03 — THE ART OF COMPRESSION
Take only the maximum from each 2×2 region, halving the size. This is the secret to recognizing a cat wherever it is — translation invariance.
04 — DRAW IT YOURSELF
Draw freely below. On the right, a CNN edge detection filter runs in real time.
05 — THE REVOLUTION
In 2012, AlexNet won ImageNet by a crushing margin. Then VGGNet, GoogLeNet, and ResNet followed — eventually surpassing even human error rates (5%).
1998–2015. The convolution era. Deeper, wider. Residual connections broke through 152 layers.
Attention only, no convolutions. The Transformer from Part II applied to vision. Challenging CNN's throne.
06 — THE CONNECTION
| Human | CNN | |
|---|---|---|
| 👁️ | Sensor — 130 million photoreceptors | Sensor — Pixels (RGB values) |
| 🧠 | Processing — V1→V2→V4→IT | Processing — Conv→Pool→Conv→... |
| 🔍 | Attention — Fovea + peripheral | Attention — Kernel sliding |
| 💫 | Illusions — Optical illusions, pareidolia | Illusions — Adversarial patches, DeepDream |
| 👶 | Learning — Generalizes from few exposures | Learning — Needs millions of images |
A machine's eye holds no sense of beauty.
No awe at a sunset, no warmth in recognizing a loved one's face.
Only grids of numbers, sliding kernels, and stacked feature maps.
Perhaps that is not seeing.
But machines find tumors, classify stars, and recognize your face —
sometimes more accurately than we can.