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Deep Learning for Computer Vision

Stanford CS231n · AI · Spring 2027 · Planned

Lectures

  1. Lecture 1: Introduction to Convolutional Neural Networks for Visual Recognition (57:56)
  2. Lecture 2: Image Classification (59:31)
  3. Lecture 3: Loss Functions and Optimization (1:14:40)
  4. Lecture 4: Introduction to Neural Networks (1:13:59)
  5. Lecture 5: Convolutional Neural Networks (1:08:56)
  6. Lecture 6: Training Neural Networks I (1:20:19)
  7. Lecture 7: Training Neural Networks II (1:15:30)
  8. Lecture 8: Deep Learning Software (1:18:07)
  9. Lecture 9: CNN Architectures (1:17:40)
  10. Lecture 10: Recurrent Neural Networks (1:13:09)
  11. Lecture 11: Detection and Segmentation (1:14:26)
  12. Lecture 12: Visualizing and Understanding (1:15:47)
  13. Lecture 13: Generative Models (1:17:41)
  14. Lecture 14: Deep Reinforcement Learning (1:04:01)
  15. Lecture 15: Efficient Methods and Hardware for Deep Learning (1:16:52)
  16. Lecture 16: Adversarial Examples and Adversarial Training (1:21:45)

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