CNN Pattern Detection Tutorial

A Convolutional Neural Network (CNN) is like a smart filter engine:

It looks at small patches of an image.
Learns filters (kernels) that detect patterns like edges, textures, or shapes.
These filters start randomly, but during training, the network adjusts them so they capture useful patterns for tasks like classification.

We’ll use a small 4×4 grayscale image and 1 convolutional filter (3×3):

Input Image:

[[1, 2, 3, 0],
[4, 5, 6, 1],
[7, 8, 9, 2],
[0, 1, 2, 3]]

Initial Random Filter (Kernel):

[[ 0.2, -0.3, 0.5],
[-0.6, 0.1, 0.4],
[ 0.3, 0.2, -0.1]]

Step	What Happens	Why It Matters
1	Convolution Operation	Slide filter over the image → dot product at each position
2	ReLU Activation	Only keep positive signals (simulates neuron firing)
3	Flatten Output	Turn 2D feature map into 1D vector
4	Fully Connected Layer	Use weights to classify
5	Compute Loss	Difference between predicted and true label
6	Backpropagation	Adjust kernel values (learn patterns)

CNN Pattern Detection Tutorial – CNN Pattern Detection example with Simple Python