Seyed Masoud Hosseini · Overview · Study log · Ideas · Transcript · RSS feed

Deep Learning Systems

CMU 10-414, Kolter & Chen · AI · Spring 2028 · Planned

Lectures

  1. Lecture 1: Introduction and Logistics (57:54)
  2. Lecture 2: ML Refresher and Softmax Regression (1:20:00)
  3. Lecture 3 (Part I): Manual Neural Networks (53:39)
  4. Lecture 3 (Part II): Manual Neural Networks (47:41)
  5. Lecture 4: Automatic Differentiation (1:03:34)
  6. Lecture 5: Automatic Differentiation Implementation (1:05:56)
  7. Lecture 6: Fully Connected Networks, Optimization, Initialization (1:26:56)
  8. Lecture 7: Neural Network Abstractions (59:58)
  9. Lecture 8: Neural Network Library Implementation (55:49)
  10. Lecture 9: Normalization and Regularization (1:19:30)
  11. Lecture 10: Convolutional Networks (1:08:31)
  12. Lecture 11: Hardware Acceleration (45:21)
  13. Lecture 12: GPU Acceleration (44:20)
  14. Lecture 13: Hardware Acceleration Implementation (50:08)
  15. Lecture 14: Implementing Convolutions (1:19:41)
  16. Lecture 15: Training Large Models (46:37)
  17. Lecture 16: Generative Adversarial Networks (38:05)
  18. Lecture 17: Generative Adversarial Networks Implementation (35:26)
  19. Lecture 18: Sequence Modeling and Recurrent Networks (1:10:47)
  20. Lecture 19: RNN Implementation (54:34)
  21. Lecture 20: Transformers and Attention (1:10:15)
  22. Lecture 21: Transformer Implementation (1:01:12)
  23. Lecture 23: Model Deployment (42:53)
  24. Lecture 24: ML Compilation and Deployment Implementation (36:36)
  25. Deep Learning Systems Online Course Teaser (2:36)

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