Modules / Lectures
Module NameDownload
noc20_cs11_assigment_1noc20_cs11_assigment_1
noc20_cs11_assigment_10noc20_cs11_assigment_10
noc20_cs11_assigment_11noc20_cs11_assigment_11
noc20_cs11_assigment_12noc20_cs11_assigment_12
noc20_cs11_assigment_13noc20_cs11_assigment_13
noc20_cs11_assigment_2noc20_cs11_assigment_2
noc20_cs11_assigment_3noc20_cs11_assigment_3
noc20_cs11_assigment_4noc20_cs11_assigment_4
noc20_cs11_assigment_5noc20_cs11_assigment_5
noc20_cs11_assigment_6noc20_cs11_assigment_6
noc20_cs11_assigment_7noc20_cs11_assigment_7
noc20_cs11_assigment_8noc20_cs11_assigment_8
noc20_cs11_assigment_9noc20_cs11_assigment_9


Sl.No Chapter Name MP4 Download
1Lecture 01: IntroductionDownload
2Lecture 02: Feature Descriptor - IDownload
3Lecture 03: Feature Descriptor - IIDownload
4Lecture 04: Bayesian Learning - IDownload
5Lecture 05: Bayesian Learning - IIDownload
6Lecture 06: Discriminant Function - IDownload
7Lecture 07: Discriminant Function - IIDownload
8Lecture 08: Discriminant Function - IIIDownload
9Lecture 09: Linear ClassifierDownload
10Lecture 10: Linear Classifier - IIDownload
11Lecture 11: Support Vector Machine - IDownload
12Lecture 12: Support Vector Machine - IIDownload
13Lecture 13: Linear MachineDownload
14Lecture 14: Multiclass Support Vector Machine - IDownload
15Lecture 15: Multiclass Support Vector Machine -IIDownload
16Lecture 16: OptimizationDownload
17Lecture 17: Optimization Techniques in Machine LearningDownload
18Lecture 18: Nonlinear FunctionsDownload
19Lecture 19: Introduction to Neural NetworkDownload
20Lecture 20: Neural Network -IIDownload
21Lecture 21: Multilayer PerceptronDownload
22Lecture 22: Multilayer Perceptron - IIDownload
23Lecture 23: Backpropagation LearningDownload
24Lecture 24: Loss FunctionDownload
25Lecture 25: Backpropagation Learning- ExampleDownload
26Lecture 26: Backpropagation Learning- Example IIDownload
27Lecture 27: Backpropagation Learning- Example IIIDownload
28Lecture 28: AutoencoderDownload
29Lecture 29: Autoencoder Vs. PCA IDownload
30Lecture 30: Autoencoder Vs. PCA IIDownload
31Lecture 31: Autoencoder TrainingDownload
32Lecture 32: Autoencoder Variants IDownload
33Lecture 33: Autoencoder Variants IIDownload
34Lecture 34: ConvolutionDownload
35Lecture 35: Cross CorrelationDownload
36Lecture 36: CNN ArchitectureDownload
37Lecture 37: MLP versus CNN, Popular CNN Architecture: LeNetDownload
38Lecture 38: Popular CNN Architecture: AlexNetDownload
39Lecture 39: Popular CNN Architecture: VGG16, Transfer LearningDownload
40Lecture 40: Vanishing and Exploding GradientDownload
41Lecture 41 : GoogleNetDownload
42Lecture 42 : ResNet, Optimisers: Momentum OptimiserDownload
43Lecture 43 : Optimisers: Momentum and Nesterov Accelerated Gradient (NAG) OptimiserDownload
44Lecture 44 : Optimisers: Adagrad OptimiserDownload
45Lecture 45 : Optimisers: RMSProp, AdaDelta and Adam OptimiserDownload
46Lecture 46: NormalizationDownload
47Lecture 47: Batch Normalization-IDownload
48Lecture 48: Batch Normalization-IIDownload
49Lecture 49: Layer, Instance, Group NormalizationDownload
50Lecture 50: Training Trick, Regularization,Early Stopping Download
51Lecture 51 : Face RecognitionDownload
52Lecture 52 : Deconvolution LayerDownload
53Lecture 53: Semantic Segmentation - IDownload
54Lecture 54: Semantic Segmentation - IIDownload
55Lecture 55: Semantic Segmentation - IIIDownload
56Lecture 56 : Image DenoisingDownload
57Lecture 57 : Variational AutoencoderDownload
58Lecture 58 : Variational Autoencoder - IIDownload
59Lecture 59 : Variational Autoencoder - IIIDownload
60Lecture 60 : Generative Adversarial NetworkDownload

Sl.No Chapter Name English
1Lecture 01: IntroductionDownload
To be verified
2Lecture 02: Feature Descriptor - IDownload
To be verified
3Lecture 03: Feature Descriptor - IIDownload
To be verified
4Lecture 04: Bayesian Learning - IDownload
To be verified
5Lecture 05: Bayesian Learning - IIDownload
To be verified
6Lecture 06: Discriminant Function - IDownload
To be verified
7Lecture 07: Discriminant Function - IIDownload
To be verified
8Lecture 08: Discriminant Function - IIIDownload
To be verified
9Lecture 09: Linear ClassifierDownload
To be verified
10Lecture 10: Linear Classifier - IIDownload
To be verified
11Lecture 11: Support Vector Machine - IDownload
To be verified
12Lecture 12: Support Vector Machine - IIDownload
To be verified
13Lecture 13: Linear MachineDownload
To be verified
14Lecture 14: Multiclass Support Vector Machine - IDownload
To be verified
15Lecture 15: Multiclass Support Vector Machine -IIDownload
To be verified
16Lecture 16: OptimizationDownload
To be verified
17Lecture 17: Optimization Techniques in Machine LearningDownload
To be verified
18Lecture 18: Nonlinear FunctionsDownload
To be verified
19Lecture 19: Introduction to Neural NetworkDownload
To be verified
20Lecture 20: Neural Network -IIDownload
To be verified
21Lecture 21: Multilayer PerceptronDownload
To be verified
22Lecture 22: Multilayer Perceptron - IIDownload
To be verified
23Lecture 23: Backpropagation LearningDownload
To be verified
24Lecture 24: Loss FunctionDownload
To be verified
25Lecture 25: Backpropagation Learning- ExampleDownload
To be verified
26Lecture 26: Backpropagation Learning- Example IIDownload
To be verified
27Lecture 27: Backpropagation Learning- Example IIIDownload
To be verified
28Lecture 28: AutoencoderDownload
To be verified
29Lecture 29: Autoencoder Vs. PCA IDownload
To be verified
30Lecture 30: Autoencoder Vs. PCA IIDownload
To be verified
31Lecture 31: Autoencoder TrainingDownload
To be verified
32Lecture 32: Autoencoder Variants IDownload
To be verified
33Lecture 33: Autoencoder Variants IIDownload
To be verified
34Lecture 34: ConvolutionDownload
To be verified
35Lecture 35: Cross CorrelationDownload
To be verified
36Lecture 36: CNN ArchitectureDownload
To be verified
37Lecture 37: MLP versus CNN, Popular CNN Architecture: LeNetDownload
To be verified
38Lecture 38: Popular CNN Architecture: AlexNetDownload
To be verified
39Lecture 39: Popular CNN Architecture: VGG16, Transfer LearningDownload
To be verified
40Lecture 40: Vanishing and Exploding GradientDownload
To be verified
41Lecture 41 : GoogleNetDownload
To be verified
42Lecture 42 : ResNet, Optimisers: Momentum OptimiserDownload
To be verified
43Lecture 43 : Optimisers: Momentum and Nesterov Accelerated Gradient (NAG) OptimiserDownload
To be verified
44Lecture 44 : Optimisers: Adagrad OptimiserDownload
To be verified
45Lecture 45 : Optimisers: RMSProp, AdaDelta and Adam OptimiserDownload
To be verified
46Lecture 46: NormalizationDownload
To be verified
47Lecture 47: Batch Normalization-IDownload
To be verified
48Lecture 48: Batch Normalization-IIDownload
To be verified
49Lecture 49: Layer, Instance, Group NormalizationDownload
To be verified
50Lecture 50: Training Trick, Regularization,Early Stopping Download
To be verified
51Lecture 51 : Face RecognitionDownload
To be verified
52Lecture 52 : Deconvolution LayerDownload
To be verified
53Lecture 53: Semantic Segmentation - IDownload
To be verified
54Lecture 54: Semantic Segmentation - IIDownload
To be verified
55Lecture 55: Semantic Segmentation - IIIDownload
To be verified
56Lecture 56 : Image DenoisingDownload
To be verified
57Lecture 57 : Variational AutoencoderDownload
To be verified
58Lecture 58 : Variational Autoencoder - IIDownload
To be verified
59Lecture 59 : Variational Autoencoder - IIIDownload
To be verified
60Lecture 60 : Generative Adversarial NetworkDownload
To be verified


Sl.No Language Book link
1EnglishNot Available
2BengaliNot Available
3GujaratiNot Available
4HindiNot Available
5KannadaNot Available
6MalayalamNot Available
7MarathiNot Available
8TamilNot Available
9TeluguNot Available