Quest 10 of 15
Convolutional networks & computer vision code
CNNs use convolution, pooling, and translation invariance for images — build a small CNN in PyTorch on CIFAR-10 or a custom defect dataset.
Start here
This module builds on earlier parts, but every important idea is explained in context. CNNs use convolution, pooling, and translation invariance for images — build a small CNN in PyTorch on CIFAR-10 or a custom defect dataset. As you read, connect each concept to the worked example and ask what a person must still decide.
Big idea
CNNs use convolution, pooling, and translation invariance for images — build a small CNN in PyTorch on CIFAR-10 or a custom defect dataset.
Learn one idea at a time
Read, explore, then mark each idea when you can explain it.
Idea 1 of 8
A convolutional layer slides learnable kernels across an image to detect local patterns such as edges and textures. Stack convolution, non-linearity, and downsampling blocks while printing tensor shapes after each stage.
Choose a deep dive
Open the topics you want to explore. The detail stays folded until you need it.
Deep dive 1Augmentation is free data
Random crops teach scale invariance; excessive augmentation can hurt fine defect detection — tune per domain.
Deep dive 2Transfer learning saves compute
ImageNet weights give strong features for industrial inspection with small labelled sets.
Deep dive 3Worked example: Convolutional networks & computer vision code
Model perfect on train images but fails on slightly rotated production photos. A responsible response is: Add rotation/affine augmentation and retrain; collect more edge cases. Correct. Use this case to separate what the technology contributes from what people contribute. The team should compare the intended outcome with a baseline where applicable, record important assumptions, and keep a clear route to correct or stop the process.
Deep dive 4Local check for Convolutional networks & computer vision code
Ask whether the examples, data, and assumptions fit your school, company, or community. Generic demos often miss local names, laws, connectivity, and languages.
Deep dive 5CNNs for inspection
Transfer learning helps when labelled industrial images are scarce.
One-minute challenge
Connect this lesson to real life
Name one situation where this idea could help, and one thing a person should still check.
Explore a real-world example
Use the arrows to connect the idea to a visible situation.
Photo example
CNN pipeline
Image → augment → conv blocks → classifier → logits → softmax.
Key terms
Tap a term to flip and read the definition.
Optional further learningFree textbooks and trusted online resources
These sources informed the course structure. Use them to revisit a concept or study it in more depth.
Ready check
Tick each idea only when you could explain it without looking back.
Ready for practice? Fine-tune a torchvision model on 200 images per class.
Extra context (audience, logistics, curriculum notes)
Built for: Software developers and technical learners — Python and coding required.
Formats: Learn scroll · Practice interactions · Quiz · Optional chat lab
Module 10 — Convolutional networks & computer vision code
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