Lecture 24 | Generative Adversarial Networks (GANs) 2 by Ben Striner

Lecture 30 of 50 in Introduction to Deep Learning (CMU 11-785) by Carnegie Mellon University Deep Learning. 1 h 33 min. Watch on YouTube

Course: Introduction to Deep Learning (CMU 11-785) (Computer Science). Covers: deep learning backpropagation optimization cnn rnn attention.

All lectures in Introduction to Deep Learning (CMU 11-785)

  1. Lecture 0: Intro to Deep Learning, Logistics and Overview. · 43 min
  2. Lecture 1 | Introduction · 1 h 11 min
  3. Lecture 2 | What Can a Network Represent · 1 h 20 min
  4. Lecture 3 | Learning the Network · 1 h 27 min
  5. Lecture 4 | Learning the Network pt .2 · 1 h 26 min
  6. Lecture 5 | Learning the Network pt .3 · 1 h 23 min
  7. Lecture 5 | Learning the Network pt .4 · 46 min
  8. Lecture 6 | Convergence · 1 h 26 min
  9. Lecture 7 | Optimization · 1 h 29 min
  10. Lecture 8 | Normalization, Regularization etc. · 1 h 25 min
  11. Lecture 8 | Normalization, Regularization etc. pt2 · 20 min
  12. Lecture 9 | CNNs · 1 h 26 min
  13. Lecture 10 | CNNs pt 2 · 1 h 23 min
  14. Lecture 11 | CNNs pt 3 · 1 h 25 min
  15. Lecture 12 | CNNs pt 4 · 1 h 27 min
  16. CNN Office Hours · 42 min
  17. Cascade-Correlation and Deep Learning | Guest Lecturer Scott E. Fahlman · 1 h 18 min
  18. Lecture 13 | RNNs · 1 h 22 min
  19. Lecture 13p2 | RNNs · 17 min
  20. Lecture 14 | RNNs p2 · 1 h 26 min
  21. Lecture 15 | RNNs p3 · 1 h 52 min
  22. Lecture 16 | CTC and seq2seq · 1 h 37 min
  23. Lecture 17p1 | CTC and seq2seq p2 · 1 h 22 min
  24. Lecture 18 | Attention · 1 h 27 min
  25. Lecture 19 | Representations · 1 h 39 min
  26. End-to-End Speech Recognition by Following my Development History | Guest Lecturer Shinji Watanabe · 1 h 29 min
  27. Lecture 21 | VAEs · 1 h 48 min
  28. Lecture 22 | VAEs 2 · 1 h 47 min
  29. Lecture 23 | Generative Adversarial Networks (GANs) 1 by Ben Striner · 1 h 30 min
  30. Lecture 24 | Generative Adversarial Networks (GANs) 2 by Ben Striner · 1 h 33 min
  31. Lecture 25 | Experimental Design for Machine Learning by Gerald Friedland · 1 h 32 min
  32. Extra Lecture 1 | Boltzmann Machines / Hopfield Nets p1 · 1 h 1 min
  33. Extra Lecture 2 | Boltzmann Machines / Hopfield Nets p2 · 1 h 2 min
  34. Extra Lecture 3 | Boltzmann Machines / Hopfield Nets p3 · 1 h 16 min
  35. Recitation0-A: Fundamentals of Python by Mansi A. · 26 min
  36. Recitation0-B: Fundamentals of NumPy by Reshmi G. · 21 min
  37. Recitation0-C - Fundamentals of PyTorch by Kushal S. · 1 h 1 min
  38. Recitation0D: AWS Setup by Faris A. · 38 min
  39. Recitation0-E: Setting up Google Colab and useful hacks by Mansi A. · 15 min
  40. Recitation0-F: Debugging in Deep Learning - 1 by Akshat G. · 7 min
  41. Recitation0F: Remote Notebooks by Anxiang Z. · 19 min
  42. Recitation 2 | Computing Derivatives by Antioch S. and Jacob L. · 1 h 31 min
  43. Bootcamp - 11785 CMU, HW1p1 · 1 h 19 min
  44. Recitation 4 | CNN Basics · 57 min
  45. Bootcamp - 11785 CMU, HW2 · 26 min
  46. Recitation 5 | CNN: Losses, Transfer Learning · 46 min
  47. Recitation 6 | RNN p1 · 11 min
  48. Recitation 7 | CTC · 53 min
  49. Recitation 8p1 | Attention · 1 h 15 min
  50. Bootcamp - 11785 CMU, HW3 · 1 h 0 min