Modules / Lectures


New Assignments
Module NameDownload
W0A1W0A1
W10A1W10A1
W11A1W11A1
W12A1W12A1
W1A1W1A1
W3A1W3A1
W4A1W4A1
W5A1W5A1
W6A1W6A1
W7A1W7A1
W8A1W8A1
W9A1W9A1

Sl.No Chapter Name English
1Lecture 1 : Introduction to Visual ComputingPDF unavailable
2Lecture 2 : Feature Extraction for Visual ComputingPDF unavailable
3Lecture 3: Feature Extraction with PythonPDF unavailable
4Lecture 4: Neural Networks for Visual ComputingPDF unavailable
5Lecture 5: Classification with Perceptron ModelPDF unavailable
6Lecture 6 : Introduction to Deep Learning with Neural NetworksPDF unavailable
7Lecture 7 : Introduction to Deep Learning with Neural NetworksPDF unavailable
8Lecture 8 : Multilayer Perceptron and Deep Neural NetworksPDF unavailable
9Lecture 9 : Multilayer Perceptron and Deep Neural NetworksPDF unavailable
10Lecture 10 : Classification with Multilayer PerceptronPDF unavailable
11Lecture 11 : Autoencoder for Representation Learning and MLP InitializationPDF unavailable
12Lecture 12 : MNIST handwritten digits classification using autoencodersPDF unavailable
13Lecture 13 ; Fashion MNIST classification using autoencodersPDF unavailable
14Lecture 14 : ALL-IDB Classification using autoencodersPDF unavailable
15Lecture 15 : Retinal Vessel Detection using autoencodersPDF unavailable
16Lecture 16 : Stacked AutoencodersPDF unavailable
17Lecture 17 : MNIST and Fashion MNIST with Stacked AutoencodersPDF unavailable
18Lecture 18 : Denoising and Sparse AutoencodersPDF unavailable
19Lecture 19 : Sparse Autoencoders for MNIST classificationPDF unavailable
20Lecture 20 : Denoising Autoencoders for MNIST classificationPDF unavailable
21Lecture 21 : Cost FunctionPDF unavailable
22Lecture 22 : Classification cost functionsPDF unavailable
23Lecture 23 : Optimization Techniques and Learning RulesPDF unavailable
24Lecture 24 : Gradient Descent Learning RulePDF unavailable
25Lecture 25 : SGD and ADAM Learning RulesPDF unavailable
26Lecture 26 : Convolutional Neural Network Building BlocksPDF unavailable
27Lecture 27 : Simple CNN Model: LeNetPDF unavailable
28Lecture 28 : LeNet DefinitionPDF unavailable
29Lecture 29 : Training a LeNet for MNIST ClassificationPDF unavailable
30Lecture 30 : Modifying a LeNet for CIFARPDF unavailable
31Lecture 31 : Convolutional Autoencoder and Deep CNNPDF unavailable
32Lecture 32 : Convolutional Autoencoder for Representation LearningPDF unavailable
33Lecture 33 : AlexNetPDF unavailable
34Lecture 34 : VGGNetPDF unavailable
35Lecture 35 : Revisiting AlexNet and VGGNet for Computational ComplexityPDF unavailable
36Lecture 36: GoogLeNet - Going very deep with convolutionsPDF unavailable
37Lecture 37 : GoogLeNetPDF unavailable
38Lecture 38: ResNet - Residual Connections within Very Deep Networks and DenseNet - Densely connected networksPDF unavailable
39Lecture 39: ResNetPDF unavailable
40Lecture 40: : DenseNetPDF unavailable
41Lecture 41 : Space and Computational Complexity in DNNPDF unavailable
42Lecture 42 : Assessing the space and computational complexity of very deep CNNsPDF unavailable
43Lecture 43: Domain Adaptation and Transfer Learning in Deep Neural NetworksPDF unavailable
44Lecture 44 : Transfer Learning a GoogLeNetPDF unavailable
45Lecture 45 : Transfer Learning a ResNetPDF unavailable
46Lecture 46 Activation pooling for object localizationPDF unavailable
47Lecture 47: Region Proposal Networks (rCNN and Faster rCNN)PDF unavailable
48Lecture 48:GAP + rCNNPDF unavailable
49Lecture 49: Semantic Segmentation with CNNPDF unavailable
50Lecture 50: UNet and SegNet for Semantic SegmentationPDF unavailable
51Lecture 51 : Autoencoders and Latent SpacesPDF unavailable
52Lecture 52 : Principle of Generative ModelingPDF unavailable
53Lecture 53 : Adversarial AutoencodersPDF unavailable
54Lecture 54 : Adversarial Autoencoder for Synthetic Sample GenerationPDF unavailable
55Lecture 55: Adversarial Autoencoder for ClassificationPDF unavailable
56Lecture 56 : Understanding Video AnalysisPDF unavailable
57Lecture 57 : Recurrent Neural Networks and Long Short-Term MemoryPDF unavailable
58Lecture 58 : Spatio-Temporal Deep Learning for Video AnalysisPDF unavailable
59Lecture 59 : Activity recognition using 3D-CNNPDF unavailable
60Lecture 60 : Activity recognition using CNN-LSTMPDF unavailable


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