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Computer Vision with Deep Learning

Image Fundamentals

Images as numbers, convolution as a sliding weighted sum, and why a CNN learns its own filters.

Level
Beginner
Lessons
3
Total time
48 min
Simulations
4
1 free lessonIncluded in Computer Vision with Deep Learning · $9
Tools you'll use
  • Python
  • PyTorch
  • OpenCV
Skills
  • Pixels & channels
  • Convolution
  • Augmentation
Try it now — free

This is the real simulation from the module. No account needed.

What you will be able to do

  • Explain an image as an H × W × C tensor
  • Compute a convolution and its output size
  • Choose augmentations that do not change the label

Before you start

A suggestion, never a lock — you can start here.

  • neural-network-foundations
Modules that help first

Syllabus

3 lessons · 48 min

Every lesson's title and summary is public. The lessons themselves open once you own the track. The lesson marked free can be read in full right now, no account needed.

Every lesson in Computer Vision with Deep Learning

  1. How Computers See Images

    18 min

    To a computer an image is a table of numbers. Convolution — a small weighted sum slid across it — turns those numbers into edges, textures and eventually objects.

    FreeRead it free Simulation

  2. Image Preprocessing and Augmentation

    16 min

    Resizing, normalisation and augmentation that make a model robust to the real world.

    Included with the track Simulation

  3. Pooling and Receptive Fields

    14 min

    How deeper layers come to "see" larger parts of the image.

    Included with the track Simulation
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