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Sl.No Chapter Name MP4 Download Transcript Download
1Lecture 01: IntroductionDownloadPDF unavailable
2Lecture 02: Feature Descriptor - IDownloadPDF unavailable
3Lecture 03: Feature Descriptor - IIDownloadPDF unavailable
4Lecture 04: Bayesian Learning - IDownloadPDF unavailable
5Lecture 05: Bayesian Learning - IIDownloadPDF unavailable
6Lecture 06: Discriminant Function - IDownloadPDF unavailable
7Lecture 07: Discriminant Function - IIDownloadPDF unavailable
8Lecture 08: Discriminant Function - IIIDownloadPDF unavailable
9Lecture 09: Linear ClassifierDownloadPDF unavailable
10Lecture 10: Linear Classifier - IIDownloadPDF unavailable
11Lecture 11: Support Vector Machine - IDownloadPDF unavailable
12Lecture 12: Support Vector Machine - IIDownloadPDF unavailable
13Lecture 13: Linear MachineDownloadPDF unavailable
14Lecture 14: Multiclass Support Vector Machine - IDownloadPDF unavailable
15Lecture 15: Multiclass Support Vector Machine -IIDownloadPDF unavailable
16Lecture 16: OptimizationDownloadPDF unavailable
17Lecture 17: Optimization Techniques in Machine LearningDownloadPDF unavailable
18Lecture 18: Nonlinear FunctionsDownloadPDF unavailable
19Lecture 19: Introduction to Neural NetworkDownloadPDF unavailable
20Lecture 20: Neural Network -IIDownloadPDF unavailable
21Lecture 21: Multilayer PerceptronDownloadPDF unavailable
22Lecture 22: Multilayer Perceptron - IIDownloadPDF unavailable
23Lecture 23: Backpropagation LearningDownloadPDF unavailable
24Lecture 24: Loss FunctionDownloadPDF unavailable
25Lecture 25: Backpropagation Learning- ExampleDownloadPDF unavailable
26Lecture 26: Backpropagation Learning- Example IIDownloadPDF unavailable
27Lecture 27: Backpropagation Learning- Example IIIDownloadPDF unavailable
28Lecture 28: AutoencoderDownloadPDF unavailable
29Lecture 29: Autoencoder Vs. PCA IDownloadPDF unavailable
30Lecture 30: Autoencoder Vs. PCA IIDownloadPDF unavailable
31Lecture 31: Autoencoder TrainingDownloadPDF unavailable
32Lecture 32: Autoencoder Variants IDownloadPDF unavailable
33Lecture 33: Autoencoder Variants IIDownloadPDF unavailable
34Lecture 34: ConvolutionDownloadPDF unavailable
35Lecture 35: Cross CorrelationDownloadPDF unavailable
36Lecture 36: CNN ArchitectureDownloadPDF unavailable
37Lecture 37: MLP versus CNN, Popular CNN Architecture: LeNetDownloadPDF unavailable
38Lecture 38: Popular CNN Architecture: AlexNetDownloadPDF unavailable
39Lecture 39: Popular CNN Architecture: VGG16, Transfer LearningDownloadPDF unavailable
40Lecture 40: Vanishing and Exploding GradientDownloadPDF unavailable
41Lecture 41 : GoogleNetDownloadPDF unavailable
42Lecture 42 : ResNet, Optimisers: Momentum OptimiserDownloadPDF unavailable
43Lecture 43 : Optimisers: Momentum and Nesterov Accelerated Gradient (NAG) OptimiserDownloadPDF unavailable
44Lecture 44 : Optimisers: Adagrad OptimiserDownloadPDF unavailable
45Lecture 45 : Optimisers: RMSProp, AdaDelta and Adam OptimiserDownloadPDF unavailable
46Lecture 46: NormalizationDownloadPDF unavailable
47Lecture 47: Batch Normalization-IDownloadPDF unavailable
48Lecture 48: Batch Normalization-IIDownloadPDF unavailable
49Lecture 49: Layer, Instance, Group NormalizationDownloadPDF unavailable
50Lecture 50: Training Trick, Regularization,Early Stopping DownloadPDF unavailable
51Lecture 51 : Face RecognitionDownloadPDF unavailable
52Lecture 52 : Deconvolution LayerDownloadPDF unavailable
53Lecture 53: Semantic Segmentation - IDownloadPDF unavailable
54Lecture 54: Semantic Segmentation - IIDownloadPDF unavailable
55Lecture 55: Semantic Segmentation - IIIDownloadPDF unavailable
56Lecture 56 : Image DenoisingDownloadPDF unavailable
57Lecture 57 : Variational AutoencoderDownloadPDF unavailable
58Lecture 58 : Variational Autoencoder - IIDownloadPDF unavailable
59Lecture 59 : Variational Autoencoder - IIIDownloadPDF unavailable
60Lecture 60 : Generative Adversarial NetworkDownloadPDF unavailable