Modules / Lectures
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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: IntroductionPDF unavailable
2Lecture 02: Feature Descriptor - IPDF unavailable
3Lecture 03: Feature Descriptor - IIPDF unavailable
4Lecture 04: Bayesian Learning - IPDF unavailable
5Lecture 05: Bayesian Learning - IIPDF unavailable
6Lecture 06: Discriminant Function - IPDF unavailable
7Lecture 07: Discriminant Function - IIPDF unavailable
8Lecture 08: Discriminant Function - IIIPDF unavailable
9Lecture 09: Linear ClassifierPDF unavailable
10Lecture 10: Linear Classifier - IIPDF unavailable
11Lecture 11: Support Vector Machine - IPDF unavailable
12Lecture 12: Support Vector Machine - IIPDF unavailable
13Lecture 13: Linear MachinePDF unavailable
14Lecture 14: Multiclass Support Vector Machine - IPDF unavailable
15Lecture 15: Multiclass Support Vector Machine -IIPDF unavailable
16Lecture 16: OptimizationPDF unavailable
17Lecture 17: Optimization Techniques in Machine LearningPDF unavailable
18Lecture 18: Nonlinear FunctionsPDF unavailable
19Lecture 19: Introduction to Neural NetworkPDF unavailable
20Lecture 20: Neural Network -IIPDF unavailable
21Lecture 21: Multilayer PerceptronPDF unavailable
22Lecture 22: Multilayer Perceptron - IIPDF unavailable
23Lecture 23: Backpropagation LearningPDF unavailable
24Lecture 24: Loss FunctionPDF unavailable
25Lecture 25: Backpropagation Learning- ExamplePDF unavailable
26Lecture 26: Backpropagation Learning- Example IIPDF unavailable
27Lecture 27: Backpropagation Learning- Example IIIPDF unavailable
28Lecture 28: AutoencoderPDF unavailable
29Lecture 29: Autoencoder Vs. PCA IPDF unavailable
30Lecture 30: Autoencoder Vs. PCA IIPDF unavailable
31Lecture 31: Autoencoder TrainingPDF unavailable
32Lecture 32: Autoencoder Variants IPDF unavailable
33Lecture 33: Autoencoder Variants IIPDF unavailable
34Lecture 34: ConvolutionPDF unavailable
35Lecture 35: Cross CorrelationPDF unavailable
36Lecture 36: CNN ArchitecturePDF unavailable
37Lecture 37: MLP versus CNN, Popular CNN Architecture: LeNetPDF unavailable
38Lecture 38: Popular CNN Architecture: AlexNetPDF unavailable
39Lecture 39: Popular CNN Architecture: VGG16, Transfer LearningPDF unavailable
40Lecture 40: Vanishing and Exploding GradientPDF unavailable
41Lecture 41 : GoogleNetPDF unavailable
42Lecture 42 : ResNet, Optimisers: Momentum OptimiserPDF unavailable
43Lecture 43 : Optimisers: Momentum and Nesterov Accelerated Gradient (NAG) OptimiserPDF unavailable
44Lecture 44 : Optimisers: Adagrad OptimiserPDF unavailable
45Lecture 45 : Optimisers: RMSProp, AdaDelta and Adam OptimiserPDF unavailable
46Lecture 46: NormalizationPDF unavailable
47Lecture 47: Batch Normalization-IPDF unavailable
48Lecture 48: Batch Normalization-IIPDF unavailable
49Lecture 49: Layer, Instance, Group NormalizationPDF unavailable
50Lecture 50: Training Trick, Regularization,Early Stopping PDF unavailable
51Lecture 51 : Face RecognitionPDF unavailable
52Lecture 52 : Deconvolution LayerPDF unavailable
53Lecture 53: Semantic Segmentation - IPDF unavailable
54Lecture 54: Semantic Segmentation - IIPDF unavailable
55Lecture 55: Semantic Segmentation - IIIPDF unavailable
56Lecture 56 : Image DenoisingPDF unavailable
57Lecture 57 : Variational AutoencoderPDF unavailable
58Lecture 58 : Variational Autoencoder - IIPDF unavailable
59Lecture 59 : Variational Autoencoder - IIIPDF unavailable
60Lecture 60 : Generative Adversarial NetworkPDF unavailable


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2BengaliNot Available
3GujaratiNot Available
4HindiNot Available
5KannadaNot Available
6MalayalamNot Available
7MarathiNot Available
8TamilNot Available
9TeluguNot Available