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