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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: IntroductionDownload
Verified
2Lecture 02: Feature Descriptor - IDownload
Verified
3Lecture 03: Feature Descriptor - IIDownload
Verified
4Lecture 04: Bayesian Learning - IDownload
Verified
5Lecture 05: Bayesian Learning - IIDownload
Verified
6Lecture 06: Discriminant Function - IDownload
Verified
7Lecture 07: Discriminant Function - IIDownload
Verified
8Lecture 08: Discriminant Function - IIIDownload
Verified
9Lecture 09: Linear ClassifierDownload
Verified
10Lecture 10: Linear Classifier - IIDownload
Verified
11Lecture 11: Support Vector Machine - IDownload
Verified
12Lecture 12: Support Vector Machine - IIDownload
Verified
13Lecture 13: Linear MachineDownload
Verified
14Lecture 14: Multiclass Support Vector Machine - IDownload
Verified
15Lecture 15: Multiclass Support Vector Machine -IIDownload
Verified
16Lecture 16: OptimizationDownload
Verified
17Lecture 17: Optimization Techniques in Machine LearningDownload
Verified
18Lecture 18: Nonlinear FunctionsDownload
Verified
19Lecture 19: Introduction to Neural NetworkDownload
Verified
20Lecture 20: Neural Network -IIDownload
Verified
21Lecture 21: Multilayer PerceptronDownload
Verified
22Lecture 22: Multilayer Perceptron - IIDownload
Verified
23Lecture 23: Backpropagation LearningDownload
Verified
24Lecture 24: Loss FunctionDownload
Verified
25Lecture 25: Backpropagation Learning- ExampleDownload
Verified
26Lecture 26: Backpropagation Learning- Example IIDownload
Verified
27Lecture 27: Backpropagation Learning- Example IIIDownload
Verified
28Lecture 28: AutoencoderDownload
Verified
29Lecture 29: Autoencoder Vs. PCA IDownload
Verified
30Lecture 30: Autoencoder Vs. PCA IIDownload
Verified
31Lecture 31: Autoencoder TrainingDownload
Verified
32Lecture 32: Autoencoder Variants IDownload
Verified
33Lecture 33: Autoencoder Variants IIDownload
Verified
34Lecture 34: ConvolutionDownload
Verified
35Lecture 35: Cross CorrelationDownload
Verified
36Lecture 36: CNN ArchitectureDownload
Verified
37Lecture 37: MLP versus CNN, Popular CNN Architecture: LeNetDownload
Verified
38Lecture 38: Popular CNN Architecture: AlexNetDownload
Verified
39Lecture 39: Popular CNN Architecture: VGG16, Transfer LearningDownload
Verified
40Lecture 40: Vanishing and Exploding GradientDownload
Verified
41Lecture 41 : GoogleNetDownload
Verified
42Lecture 42 : ResNet, Optimisers: Momentum OptimiserDownload
Verified
43Lecture 43 : Optimisers: Momentum and Nesterov Accelerated Gradient (NAG) OptimiserDownload
Verified
44Lecture 44 : Optimisers: Adagrad OptimiserDownload
Verified
45Lecture 45 : Optimisers: RMSProp, AdaDelta and Adam OptimiserDownload
Verified
46Lecture 46: NormalizationDownload
Verified
47Lecture 47: Batch Normalization-IDownload
Verified
48Lecture 48: Batch Normalization-IIDownload
Verified
49Lecture 49: Layer, Instance, Group NormalizationDownload
Verified
50Lecture 50: Training Trick, Regularization,Early Stopping Download
Verified
51Lecture 51 : Face RecognitionDownload
Verified
52Lecture 52 : Deconvolution LayerDownload
Verified
53Lecture 53: Semantic Segmentation - IDownload
Verified
54Lecture 54: Semantic Segmentation - IIDownload
Verified
55Lecture 55: Semantic Segmentation - IIIDownload
Verified
56Lecture 56 : Image DenoisingDownload
Verified
57Lecture 57 : Variational AutoencoderDownload
Verified
58Lecture 58 : Variational Autoencoder - IIDownload
Verified
59Lecture 59 : Variational Autoencoder - IIIDownload
Verified
60Lecture 60 : Generative Adversarial NetworkDownload
Verified


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