Modules / Lectures
Module NameDownloadDescriptionDownload Size
Week 1Week 0 Assignment 1Week 0 Assignment 1315 kb
Week 1Week 1 Assignment 1Week 1 Assignment 1205 kb
Week 2Week 2 Assignment 1Week 2 Assignment 1199 kb
Week 3Week 3 Assignment 1Week 3 Assignment 1203 kb
Week 4Week 4 Assignment 1Week 4 Assignment 1223 kb
Week 5Week 5 Assignment 1Week 5 Assignment 1197 kb
Week 6Week 6 Assignment 1Week 6 Assignment 1206 kb
Week 7Week 7 Assignment 1Week 7 Assignment 1201 kb
Week 8Week 8 Assignment 1Week 8 Assignment 1200 kb
Week 9Week 9 Assignment 1Week 9 Assignment 1199 kb
Week 10Week 10 Assignment 1Week 10 Assignment 1198 kb
Week 11Week 11 Assignment 1Week 11 Assignment 1238 kb
Week 12Week 12 Assignment 1Week 12 Assignment 1205 kb

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