| Name | Download | Download Size |
|---|---|---|
| Lecture Note | Download as zip file | 40M |
| Module Name | Download | Description | Download Size |
|---|---|---|---|
| WEEK 1 | noc22-cs22_week1 | ||
| WEEK 2 | noc22-cs22_week2 | ||
| WEEK 3 | noc22-cs22_week3 |
| Module Name | Download |
|---|---|
| noc20_cs11_assigment_1 | noc20_cs11_assigment_1 |
| noc20_cs11_assigment_10 | noc20_cs11_assigment_10 |
| noc20_cs11_assigment_11 | noc20_cs11_assigment_11 |
| noc20_cs11_assigment_12 | noc20_cs11_assigment_12 |
| noc20_cs11_assigment_13 | noc20_cs11_assigment_13 |
| noc20_cs11_assigment_2 | noc20_cs11_assigment_2 |
| noc20_cs11_assigment_3 | noc20_cs11_assigment_3 |
| noc20_cs11_assigment_4 | noc20_cs11_assigment_4 |
| noc20_cs11_assigment_5 | noc20_cs11_assigment_5 |
| noc20_cs11_assigment_6 | noc20_cs11_assigment_6 |
| noc20_cs11_assigment_7 | noc20_cs11_assigment_7 |
| noc20_cs11_assigment_8 | noc20_cs11_assigment_8 |
| noc20_cs11_assigment_9 | noc20_cs11_assigment_9 |
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Lecture 01: Introduction | Download |
| 2 | Lecture 02: Feature Descriptor - I | Download |
| 3 | Lecture 03: Feature Descriptor - II | Download |
| 4 | Lecture 04: Bayesian Learning - I | Download |
| 5 | Lecture 05: Bayesian Learning - II | Download |
| 6 | Lecture 06: Discriminant Function - I | Download |
| 7 | Lecture 07: Discriminant Function - II | Download |
| 8 | Lecture 08: Discriminant Function - III | Download |
| 9 | Lecture 09: Linear Classifier | Download |
| 10 | Lecture 10: Linear Classifier - II | Download |
| 11 | Lecture 11: Support Vector Machine - I | Download |
| 12 | Lecture 12: Support Vector Machine - II | Download |
| 13 | Lecture 13: Linear Machine | Download |
| 14 | Lecture 14: Multiclass Support Vector Machine - I | Download |
| 15 | Lecture 15: Multiclass Support Vector Machine -II | Download |
| 16 | Lecture 16: Optimization | Download |
| 17 | Lecture 17: Optimization Techniques in Machine Learning | Download |
| 18 | Lecture 18: Nonlinear Functions | Download |
| 19 | Lecture 19: Introduction to Neural Network | Download |
| 20 | Lecture 20: Neural Network -II | Download |
| 21 | Lecture 21: Multilayer Perceptron | Download |
| 22 | Lecture 22: Multilayer Perceptron - II | Download |
| 23 | Lecture 23: Backpropagation Learning | Download |
| 24 | Lecture 24: Loss Function | Download |
| 25 | Lecture 25: Backpropagation Learning- Example | Download |
| 26 | Lecture 26: Backpropagation Learning- Example II | Download |
| 27 | Lecture 27: Backpropagation Learning- Example III | Download |
| 28 | Lecture 28: Autoencoder | Download |
| 29 | Lecture 29: Autoencoder Vs. PCA I | Download |
| 30 | Lecture 30: Autoencoder Vs. PCA II | Download |
| 31 | Lecture 31: Autoencoder Training | Download |
| 32 | Lecture 32: Autoencoder Variants I | Download |
| 33 | Lecture 33: Autoencoder Variants II | Download |
| 34 | Lecture 34: Convolution | Download |
| 35 | Lecture 35: Cross Correlation | Download |
| 36 | Lecture 36: CNN Architecture | Download |
| 37 | Lecture 37: MLP versus CNN, Popular CNN Architecture: LeNet | Download |
| 38 | Lecture 38: Popular CNN Architecture: AlexNet | Download |
| 39 | Lecture 39: Popular CNN Architecture: VGG16, Transfer Learning | Download |
| 40 | Lecture 40: Vanishing and Exploding Gradient | Download |
| 41 | Lecture 41 : GoogleNet | Download |
| 42 | Lecture 42 : ResNet, Optimisers: Momentum Optimiser | Download |
| 43 | Lecture 43 : Optimisers: Momentum and Nesterov Accelerated Gradient (NAG) Optimiser | Download |
| 44 | Lecture 44 : Optimisers: Adagrad Optimiser | Download |
| 45 | Lecture 45 : Optimisers: RMSProp, AdaDelta and Adam Optimiser | Download |
| 46 | Lecture 46: Normalization | Download |
| 47 | Lecture 47: Batch Normalization-I | Download |
| 48 | Lecture 48: Batch Normalization-II | Download |
| 49 | Lecture 49: Layer, Instance, Group Normalization | Download |
| 50 | Lecture 50: Training Trick, Regularization,Early Stopping | Download |
| 51 | Lecture 51 : Face Recognition | Download |
| 52 | Lecture 52 : Deconvolution Layer | Download |
| 53 | Lecture 53: Semantic Segmentation - I | Download |
| 54 | Lecture 54: Semantic Segmentation - II | Download |
| 55 | Lecture 55: Semantic Segmentation - III | Download |
| 56 | Lecture 56 : Image Denoising | Download |
| 57 | Lecture 57 : Variational Autoencoder | Download |
| 58 | Lecture 58 : Variational Autoencoder - II | Download |
| 59 | Lecture 59 : Variational Autoencoder - III | Download |
| 60 | Lecture 60 : Generative Adversarial Network | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Lecture 01: Introduction | Download Verified |
| 2 | Lecture 02: Feature Descriptor - I | Download Verified |
| 3 | Lecture 03: Feature Descriptor - II | Download Verified |
| 4 | Lecture 04: Bayesian Learning - I | Download Verified |
| 5 | Lecture 05: Bayesian Learning - II | Download Verified |
| 6 | Lecture 06: Discriminant Function - I | Download Verified |
| 7 | Lecture 07: Discriminant Function - II | Download Verified |
| 8 | Lecture 08: Discriminant Function - III | Download Verified |
| 9 | Lecture 09: Linear Classifier | Download Verified |
| 10 | Lecture 10: Linear Classifier - II | Download Verified |
| 11 | Lecture 11: Support Vector Machine - I | Download Verified |
| 12 | Lecture 12: Support Vector Machine - II | Download Verified |
| 13 | Lecture 13: Linear Machine | Download Verified |
| 14 | Lecture 14: Multiclass Support Vector Machine - I | Download Verified |
| 15 | Lecture 15: Multiclass Support Vector Machine -II | Download Verified |
| 16 | Lecture 16: Optimization | Download Verified |
| 17 | Lecture 17: Optimization Techniques in Machine Learning | Download Verified |
| 18 | Lecture 18: Nonlinear Functions | Download Verified |
| 19 | Lecture 19: Introduction to Neural Network | Download Verified |
| 20 | Lecture 20: Neural Network -II | Download Verified |
| 21 | Lecture 21: Multilayer Perceptron | Download Verified |
| 22 | Lecture 22: Multilayer Perceptron - II | Download Verified |
| 23 | Lecture 23: Backpropagation Learning | Download Verified |
| 24 | Lecture 24: Loss Function | Download Verified |
| 25 | Lecture 25: Backpropagation Learning- Example | Download Verified |
| 26 | Lecture 26: Backpropagation Learning- Example II | Download Verified |
| 27 | Lecture 27: Backpropagation Learning- Example III | Download Verified |
| 28 | Lecture 28: Autoencoder | Download Verified |
| 29 | Lecture 29: Autoencoder Vs. PCA I | Download Verified |
| 30 | Lecture 30: Autoencoder Vs. PCA II | Download Verified |
| 31 | Lecture 31: Autoencoder Training | Download Verified |
| 32 | Lecture 32: Autoencoder Variants I | Download Verified |
| 33 | Lecture 33: Autoencoder Variants II | Download Verified |
| 34 | Lecture 34: Convolution | Download Verified |
| 35 | Lecture 35: Cross Correlation | Download Verified |
| 36 | Lecture 36: CNN Architecture | Download Verified |
| 37 | Lecture 37: MLP versus CNN, Popular CNN Architecture: LeNet | Download Verified |
| 38 | Lecture 38: Popular CNN Architecture: AlexNet | Download Verified |
| 39 | Lecture 39: Popular CNN Architecture: VGG16, Transfer Learning | Download Verified |
| 40 | Lecture 40: Vanishing and Exploding Gradient | Download Verified |
| 41 | Lecture 41 : GoogleNet | Download Verified |
| 42 | Lecture 42 : ResNet, Optimisers: Momentum Optimiser | Download Verified |
| 43 | Lecture 43 : Optimisers: Momentum and Nesterov Accelerated Gradient (NAG) Optimiser | Download Verified |
| 44 | Lecture 44 : Optimisers: Adagrad Optimiser | Download Verified |
| 45 | Lecture 45 : Optimisers: RMSProp, AdaDelta and Adam Optimiser | Download Verified |
| 46 | Lecture 46: Normalization | Download Verified |
| 47 | Lecture 47: Batch Normalization-I | Download Verified |
| 48 | Lecture 48: Batch Normalization-II | Download Verified |
| 49 | Lecture 49: Layer, Instance, Group Normalization | Download Verified |
| 50 | Lecture 50: Training Trick, Regularization,Early Stopping | Download Verified |
| 51 | Lecture 51 : Face Recognition | Download Verified |
| 52 | Lecture 52 : Deconvolution Layer | Download Verified |
| 53 | Lecture 53: Semantic Segmentation - I | Download Verified |
| 54 | Lecture 54: Semantic Segmentation - II | Download Verified |
| 55 | Lecture 55: Semantic Segmentation - III | Download Verified |
| 56 | Lecture 56 : Image Denoising | Download Verified |
| 57 | Lecture 57 : Variational Autoencoder | Download Verified |
| 58 | Lecture 58 : Variational Autoencoder - II | Download Verified |
| 59 | Lecture 59 : Variational Autoencoder - III | Download Verified |
| 60 | Lecture 60 : Generative Adversarial Network | Download Verified |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Download |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |