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Course10810510361 lectures

Deep Learning For Visual Computing

Prof. Debdoot Sheet · IIT Kharagpur

Start with lecture 1 At NPTEL

Lectures

Week 1

  1. Lecture 1 : Introduction to Visual Computing Video
  2. Lecture 2 : Feature Extraction for Visual Computing Video
  3. Lecture 3: Feature Extraction with Python Video
  4. Lecture 4: Neural Networks for Visual Computing Video
  5. Lecture 5: Classification with Perceptron Model Video

How does an NPTEL online course work?

  1. Lecture 6 : Introduction to Deep Learning with Neural Networks Video
  2. Lecture 7 : Introduction to Deep Learning with Neural Networks Video
  3. Lecture 8 : Multilayer Perceptron and Deep Neural Networks Video
  4. Lecture 9 : Multilayer Perceptron and Deep Neural Networks Video
  5. Lecture 10 : Classification with Multilayer Perceptron Video

Week 3

  1. Lecture 11 : Autoencoder for Representation Learning and MLP Initialization Video
  2. Lecture 12 : MNIST handwritten digits classification using autoencoders Video
  3. Lecture 13 ; Fashion MNIST classification using autoencoders Video
  4. Lecture 14 : ALL-IDB Classification using autoencoders Video
  5. Lecture 15 : Retinal Vessel Detection using autoencoders Video

Week 4

  1. Lecture 16 : Stacked Autoencoders Video
  2. Lecture 17 : MNIST and Fashion MNIST with Stacked Autoencoders Video
  3. Lecture 18 : Denoising and Sparse Autoencoders Video
  4. Lecture 19 : Sparse Autoencoders for MNIST classification Video
  5. Lecture 20 : Denoising Autoencoders for MNIST classification Video

Week 5

  1. Lecture 21 : Cost Function Video
  2. Lecture 22 : Classification cost functions Video
  3. Lecture 23 : Optimization Techniques and Learning Rules Video
  4. Lecture 24 : Gradient Descent Learning Rule Video
  5. Lecture 25 : SGD and ADAM Learning Rules Video

Week 6

  1. Lecture 26 : Convolutional Neural Network Building Blocks Video
  2. Lecture 27 : Simple CNN Model: LeNet Video
  3. Lecture 28 : LeNet Definition Video
  4. Lecture 29 : Training a LeNet for MNIST Classification Video
  5. Lecture 30 : Modifying a LeNet for CIFAR Video

Week 7

  1. Lecture 31 : Convolutional Autoencoder and Deep CNN Video
  2. Lecture 32 : Convolutional Autoencoder for Representation Learning Video
  3. Lecture 33 : AlexNet Video
  4. Lecture 34 : VGGNet Video
  5. Lecture 35 : Revisiting AlexNet and VGGNet for Computational Complexity Video

Week 8

  1. Lecture 36: GoogLeNet - Going very deep with convolutions Video
  2. Lecture 37 : GoogLeNet Video
  3. Lecture 38: ResNet - Residual Connections within Very Deep Networks and DenseNet - Densely connected networks Video
  4. Lecture 39: ResNet Video
  5. Lecture 40: : DenseNet Video

Week 9

  1. Lecture 41 : Space and Computational Complexity in DNN Video
  2. Lecture 42 : Assessing the space and computational complexity of very deep CNNs Video
  3. Lecture 43: Domain Adaptation and Transfer Learning in Deep Neural Networks Video
  4. Lecture 44 : Transfer Learning a GoogLeNet Video
  5. Lecture 45 : Transfer Learning a ResNet Video

Week 10

  1. Lecture 46 Activation pooling for object localization Video
  2. Lecture 47: Region Proposal Networks (rCNN and Faster rCNN) Video
  3. Lecture 48:GAP + rCNN Video
  4. Lecture 49: Semantic Segmentation with CNN Video
  5. Lecture 50: UNet and SegNet for Semantic Segmentation Video

Week 11

  1. Lecture 51 : Autoencoders and Latent Spaces Video
  2. Lecture 52 : Principle of Generative Modeling Video
  3. Lecture 53 : Adversarial Autoencoders Video
  4. Lecture 54 : Adversarial Autoencoder for Synthetic Sample Generation Video
  5. Lecture 55: Adversarial Autoencoder for Classification Video

Week 12

  1. Lecture 56 : Understanding Video Analysis Video
  2. Lecture 57 : Recurrent Neural Networks and Long Short-Term Memory Video
  3. Lecture 58 : Spatio-Temporal Deep Learning for Video Analysis Video
  4. Lecture 59 : Activity recognition using 3D-CNN Video
  5. Lecture 60 : Activity recognition using CNN-LSTM Video

Live Session

  1. Live Session 04-11-2020 Video

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Kharagpur
Department
Electrical Engineering
Teacher
Prof. Debdoot Sheet
Level
Undergraduate
Language
English
Subjects
Engineering
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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