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Computer Vision with Deep Learning

Vision Transformers and Multimodal Models

ViT, CLIP and vision-language models — when images and text share one embedding space.

Level
Intermediate
Lessons
3
Total time
52 min
Simulations
2
Included in Computer Vision with Deep Learning · $9
Tools you'll use
  • Python
  • PyTorch
  • OpenCV
Skills
  • ViT
  • CLIP
  • Vision-language models
Try it now — free

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

What you will be able to do

  • Explain how a ViT turns an image into tokens
  • Build image search with CLIP embeddings

Before you start

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

  • image-classification-with-cnns
Modules that help first

Syllabus

3 lessons · 52 min

Every lesson's title and summary is public. The lessons themselves open once you own the track.

Every lesson in Computer Vision with Deep Learning

  1. Vision Transformers (ViT)

    18 min

    Patches as tokens, and when a ViT beats a CNN.

    Included with the track Simulation

  2. CLIP and Image-Text Embeddings

    18 min

    Zero-shot classification and searching images with a sentence.

    Included with the track Simulation

  3. Vision-Language Models

    16 min

    LLMs that read images: what they can do, what they cannot yet, and how to test them.

    Included with the track
Lifetime access
$9
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