Deep Learning

Deep Learning

IIT Kharagpur July 2018 via YouTube Direct link

NPTEL: Deep Learning

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1 of 61

NPTEL: Deep Learning

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Deep Learning

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  1. 1 NPTEL: Deep Learning
  2. 2 Lecture 01 : Introduction
  3. 3 Lecture 02 : Feature Descriptor - I
  4. 4 Lecture 03 : Feature Descriptor - II
  5. 5 Lecture 04 : Bayesian Learning - I
  6. 6 Lecture 05 : Bayesian Learning - II
  7. 7 Lecture 06 : Discriminant Function - I
  8. 8 Lecture 07 : Discriminant Function - II
  9. 9 Lecture 08 : Discriminant Function - III
  10. 10 Lecture 09 : Linear Classifier
  11. 11 Lecture 10 : Linear Classifier - II
  12. 12 Lecture 11 : Support Vector Machine - I
  13. 13 Lecture 12 : Support Vector Machine - II
  14. 14 Lecture 13 : Linear Machine
  15. 15 Lecture 14 : Multiclass Support Vector Machine - I
  16. 16 Lecture 15 : Multiclass Support Vector Machine -II
  17. 17 Lecture 16 : Optimization
  18. 18 Lecture 17 : Optimization Techniques in Machine Learning
  19. 19 Lecture 18 : Nonlinear Functions
  20. 20 Lecture 19 : Introduction to Neural Network
  21. 21 Lecture 20 : Neural Network -II
  22. 22 Lecture 21 : Multilayer Perceptron
  23. 23 Lecture 22 : Multilayer Perceptron - II
  24. 24 Lecture 23 : Backpropagation Learning
  25. 25 Lecture 24 : Loss Function
  26. 26 Lecture 25 : Backpropagation Learning - Example
  27. 27 Lecture 26 : Backpropagation Learning- Example II
  28. 28 Lecture 27 : Backpropagation Learning- Example III
  29. 29 Lecture 28 : Autoencoder
  30. 30 Lecture 29 : Autoencoder Vs. PCA I
  31. 31 Lecture 30 : Autoencoder Vs. PCA II
  32. 32 Lecture 31 : Autoencoder Training
  33. 33 Lecture 32 : Autoencoder Variants I
  34. 34 Lecture 33 : Autoencoder Variants II
  35. 35 Lecture 34 : Convolution
  36. 36 Lecture 35 : Cross Correlation
  37. 37 Lecture 36 : CNN Architecture
  38. 38 Lecture 37 : MLP versus CNN, Popular CNN Architecture: LeNet
  39. 39 Lecture 38 : Popular CNN Architecture: AlexNet
  40. 40 Lecture 39 : Popular CNN Architecture: VGG16, Transfer Learning
  41. 41 Lecture 40 : Vanishing and Exploding Gradient
  42. 42 Lecture 41 : GoogleNet
  43. 43 Lecture 42 : ResNet, Optimisers: Momentum Optimiser
  44. 44 Lecture 43 : Optimisers: Momentum and Nesterov Accelerated Gradient (NAG) Optimiser
  45. 45 Lecture 44 : Optimisers: Adagrad Optimiser
  46. 46 Lecture 45 : Optimisers: RMSProp, AdaDelta and Adam Optimiser
  47. 47 Lecture 46 : Normalization
  48. 48 Lecture 47 : Batch Normalization-I
  49. 49 Lecture 48 : Batch Normalization-II
  50. 50 Lecture 49 : Layer, Instance, Group Normalization
  51. 51 Lecture 50 : Training Trick, Regularization,Early Stopping
  52. 52 Lecture 51 : Face Recognition
  53. 53 Lecture 52 : Deconvolution Layer
  54. 54 Lecture 53 : Semantic Segmentation - I
  55. 55 Lecture 54 : Semantic Segmentation - II
  56. 56 Lecture 55 : Semantic Segmentation - III
  57. 57 Lecture 56: Image Denoising
  58. 58 Lecture 57 : Variational Autoencoder
  59. 59 Lecture 58 : Variational Autoencoder - II
  60. 60 Lecture 59 : Variational Autoencoder - III
  61. 61 Lecture 60 : Generative Adversarial Network

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