Computer Vision

The HOOK - Temporarily kept slide

HOOK IDEA 1

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HOOK IDEA 10

Generate at least 10 possible hooks. You generally don't know what will work until you've exhausted the obvious and started to explore uncharted territory

Storyboard Your Narrative

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the takeaway

Resources used

• "What is a Convolution?Links to an external site."
•  "CNN PlaygroundLinks to an external site."
•  Convolutions SpreadsheetLinks to an external site. 
• Nodes that Notice ThingsLinks to an external site.
•  VISION 101 SpreadsheetLinks to an external site. 
•  Beyond Recognition: Representations and Adversarial Vision — "Feature VisualizationLinks to an external site.

Opening

Hey everyone. Today I want to show you something surprisingly simple: how a tiny 3×3 grid of numbers can transform a jumble of pixels into something a machine can recognize. We’ll break down what convolutions actually do, why they matter, and how they became the foundation of computer vision.

Closing

That’s the heart of the convolution revolution - simple filters, repeated many times, learning to extract meaning from pixels. Thank you for listening

Ever seen one of these?

Select all the squares with traffic lights - to prove you're human. But the crazy part? A robot is grading your work. Yep, sometimes just a simple convolution network with nothing but a little bit of math and few filters could reliably figure out if you are a human and not a robot.

Hook

Conclusion

So the next time you’re clicking boxes to prove you’re human, remember:
the machine on the other end is ‘seeing’ through stacks of tiny filters - and that simple math is what makes modern vision possible.

Why can't computers just see?

• Images essentially are just a HUGE pile of numbers (for computers)

• Light, angle, sizes constantly change. We can't just compare image of known object to our unknown image
• We are looking for a way to detect PATTERNS across images

This would give machine just as much information as you could gain from it

The Solution