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Lecture 7 of 61How does an NPTEL online course work?

Lecture 7 : Introduction to Deep Learning with Neural Networks

Prof. Debdoot Sheet · IIT Kharagpur

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Deep Learning For Visual Computing, IIT Kharagpur (at NPTEL)
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CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures

  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
  6. Lecture 6 : Introduction to Deep Learning with Neural Networks Video
  7. Lecture 7 : Introduction to Deep Learning with Neural Networks Video
  8. Lecture 8 : Multilayer Perceptron and Deep Neural Networks Video
  9. Lecture 9 : Multilayer Perceptron and Deep Neural Networks Video
  10. Lecture 10 : Classification with Multilayer Perceptron Video
  11. Lecture 11 : Autoencoder for Representation Learning and MLP Initialization Video
  12. Lecture 12 : MNIST handwritten digits classification using autoencoders Video
  13. Lecture 13 ; Fashion MNIST classification using autoencoders Video
  14. Lecture 14 : ALL-IDB Classification using autoencoders Video
  15. Lecture 15 : Retinal Vessel Detection using autoencoders Video
  16. Lecture 16 : Stacked Autoencoders Video
  17. Lecture 17 : MNIST and Fashion MNIST with Stacked Autoencoders Video
  18. Lecture 18 : Denoising and Sparse Autoencoders Video
  19. Lecture 19 : Sparse Autoencoders for MNIST classification Video
  20. Lecture 20 : Denoising Autoencoders for MNIST classification Video
  21. Lecture 21 : Cost Function Video
  22. Lecture 22 : Classification cost functions Video
  23. Lecture 23 : Optimization Techniques and Learning Rules Video
  24. Lecture 24 : Gradient Descent Learning Rule Video
  25. Lecture 25 : SGD and ADAM Learning Rules Video
  26. Lecture 26 : Convolutional Neural Network Building Blocks Video
  27. Lecture 27 : Simple CNN Model: LeNet Video
  28. Lecture 28 : LeNet Definition Video
  29. Lecture 29 : Training a LeNet for MNIST Classification Video
  30. Lecture 30 : Modifying a LeNet for CIFAR Video
  31. Lecture 31 : Convolutional Autoencoder and Deep CNN Video
  32. Lecture 32 : Convolutional Autoencoder for Representation Learning Video
  33. Lecture 33 : AlexNet Video
  34. Lecture 34 : VGGNet Video
  35. Lecture 35 : Revisiting AlexNet and VGGNet for Computational Complexity Video
  36. Lecture 36: GoogLeNet - Going very deep with convolutions Video
  37. Lecture 37 : GoogLeNet Video
  38. Lecture 38: ResNet - Residual Connections within Very Deep Networks and DenseNet - Densely connected networks Video
  39. Lecture 39: ResNet Video
  40. Lecture 40: : DenseNet Video
  41. Lecture 41 : Space and Computational Complexity in DNN Video
  42. Lecture 42 : Assessing the space and computational complexity of very deep CNNs Video
  43. Lecture 43: Domain Adaptation and Transfer Learning in Deep Neural Networks Video
  44. Lecture 44 : Transfer Learning a GoogLeNet Video
  45. Lecture 45 : Transfer Learning a ResNet Video
  46. Lecture 46 Activation pooling for object localization Video
  47. Lecture 47: Region Proposal Networks (rCNN and Faster rCNN) Video
  48. Lecture 48:GAP + rCNN Video
  49. Lecture 49: Semantic Segmentation with CNN Video
  50. Lecture 50: UNet and SegNet for Semantic Segmentation Video
  51. Lecture 51 : Autoencoders and Latent Spaces Video
  52. Lecture 52 : Principle of Generative Modeling Video
  53. Lecture 53 : Adversarial Autoencoders Video
  54. Lecture 54 : Adversarial Autoencoder for Synthetic Sample Generation Video
  55. Lecture 55: Adversarial Autoencoder for Classification Video
  56. Lecture 56 : Understanding Video Analysis Video
  57. Lecture 57 : Recurrent Neural Networks and Long Short-Term Memory Video
  58. Lecture 58 : Spatio-Temporal Deep Learning for Video Analysis Video
  59. Lecture 59 : Activity recognition using 3D-CNN Video
  60. Lecture 60 : Activity recognition using CNN-LSTM Video
  61. Live Session 04-11-2020 Video