Black Friday 2024: 25+ Ways to Save on Online Learning
Deep Learning

Deep Learning

NPTEL-NOC IITM via YouTube Direct link

Deep Learning(CS7015): Lec 9.5 Batch Normalization

74 of 118
Previous
Next

74 of 118

Deep Learning(CS7015): Lec 9.5 Batch Normalization

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Deep Learning

Automatically move to the next video in the Classroom when playback concludes
  1. 1 Deep Learning - Course Introduction
  2. 2 Deep Learning(CS7015): Lec 1.1 Biological Neuron
  3. 3 Deep Learning(CS7015): Lec 1.2 From Spring to Winter of AI
  4. 4 Deep Learning(CS7015): Lec 1.3 The Deep Revival
  5. 5 Deep Learning(CS7015): Lec 1.4 From Cats to Convolutional Neural Networks
  6. 6 Deep Learning(CS7015): Lec 1.5 Faster, higher, stronger
  7. 7 Deep Learning(CS7015): Lec 1.6 The Curious Case of Sequences
  8. 8 Deep Learning(CS7015): Lec 1.7 Beating humans at their own games (literally)
  9. 9 Deep Learning(CS7015): Lec 1.8 The Madness (2013-)
  10. 10 Deep Learning(CS7015): Lec 1.9 (Need for) Sanity
  11. 11 Deep Learning(CS7015): Lec 2.1 Motivation from Biological Neurons
  12. 12 Deep Learning(CS7015): Lec 2.2 McCulloch Pitts Neuron, Thresholding Logic
  13. 13 Deep Learning(CS7015): Lec 2.3 Perceptrons
  14. 14 Deep Learning(CS7015): Lec 2.4 Error and Error Surfaces
  15. 15 Deep Learning(CS7015): Lec 2.5 Perceptron Learning Algorithm
  16. 16 Deep Learning(CS7015): Lec 2.6 Proof of Convergence of Perceptron Learning Algorithm
  17. 17 Deep Learning(CS7015): Lec 2.7 Linearly Separable Boolean Functions
  18. 18 Deep Learning(CS7015): Lec 2.8 Representation Power of a Network of Perceptrons
  19. 19 Deep Learning(CS7015): Lec 3.1 Sigmoid Neuron
  20. 20 Deep Learning(CS7015): Lec 3.2 A typical Supervised Machine Learning Setup
  21. 21 Deep Learning(CS7015): Lec 3.3 Learning Parameters: (Infeasible) guess work
  22. 22 Deep Learning(CS7015): Lec 3.4 Learning Parameters: Gradient Descent
  23. 23 Deep Learning(CS7015): Lec 3.5 Representation Power of Multilayer Network of Sigmoid Neurons
  24. 24 Deep Learning(CS7015): Lec 4.1 Feedforward Neural Networks (a.k.a multilayered network of neurons)
  25. 25 Deep Learning(CS7015): Lec 4.2 Learning Paramters of Feedforward Neural Networks (Intuition)
  26. 26 Deep Learning(CS7015): Lec 4.3 Output functions and Loss functions
  27. 27 Deep Learning(CS7015): Lec 4.4 Backpropagation (Intuition)
  28. 28 Deep Learning(CS7015): Lec 4.5 Backpropagation: Computing Gradients w.r.t. the Output Units
  29. 29 Deep Learning(CS7015): Lec 4.6 Backpropagation: Computing Gradients w.r.t. Hidden Units
  30. 30 Deep Learning(CS7015): Lec 4.7 Backpropagation: Computing Gradients w.r.t. Parameters
  31. 31 Deep Learning(CS7015): Lec 4.8 Backpropagation: Pseudo code
  32. 32 Deep Learning(CS7015): Lec 4.9 Derivative of the activation function
  33. 33 Deep Learning(CS7015): Lec 6.6 PCA : Interpretation 3
  34. 34 Deep Learning(CS7015): Lec 4.10 Information content, Entropy & cross entropy
  35. 35 Deep Learning(CS7015): Lec 5.1 & Lec 5.2 Recap: Learning Parameters: Guess Work, Gradient Descent
  36. 36 Deep Learning(CS7015): Lec 5.3 Contours Maps
  37. 37 Deep Learning(CS7015): Lec 5.4 Momentum based Gradient Descent
  38. 38 Deep Learning(CS7015): Lec 5.5 Nesterov Accelerated Gradient Descent
  39. 39 Deep Learning(CS7015): Lec 5.6 Stochastic And Mini-Batch Gradient Descent
  40. 40 Deep Learning(CS7015): Lec 5.7 Tips for Adjusting Learning Rate and Momentum
  41. 41 Deep Learning(CS7015): Lec 5.8 Line Search
  42. 42 Deep Learning(CS7015): Lec 5.9 Gradient Descent with Adaptive Learning Rate
  43. 43 Deep Learning(CS7015): Lec 5.9 (Part-2) Bias Correction in Adam
  44. 44 Deep Learning(CS7015): Lec 6.1 Eigenvalues and Eigenvectors
  45. 45 Deep Learning(CS7015): Lec 6.2 Linear Algebra : Basic Definitions
  46. 46 Deep Learning(CS7015): Lec 6.3 Eigenvalue Decompositon
  47. 47 Deep Learning(CS7015): Lec 6.4 Principal Component Analysis and its Interpretations
  48. 48 Deep Learning(CS7015): Lec 6.5 PCA : Interpretation 2
  49. 49 Deep Learning(CS7015): Lec 6.6 (Part-2) PCA : Interpretation 3 (Contd.)
  50. 50 Deep Learning(CS7015): Lec 6.7 PCA : Practical Example
  51. 51 Deep Learning(CS7015): Lec 6.8 Singular Value Decomposition
  52. 52 Deep Learning(CS7015): Lec 7.1 Introduction to Autoncoders
  53. 53 Deep Learning(CS7015): Lec 7.2 Link between PCA and Autoencoders
  54. 54 Deep Learning(CS7015): Lec 7.3 Regularization in autoencoders (Motivation)
  55. 55 Deep Learning(CS7015): Lec 7.4 Denoising Autoencoders
  56. 56 Deep Learning(CS7015): Lec 7.5 Sparse Autoencoders
  57. 57 Deep Learning(CS7015): Lec 7.6 Contractive Autoencoders
  58. 58 Deep Learning(CS7015): Lec 8.1 Bias and Variance
  59. 59 Deep Learning(CS7015): Lec 8.2 Train error vs Test error
  60. 60 Deep Learning(CS7015): Lec 8.2 (Part-2) Train error vs Test error (Recap)
  61. 61 Deep Learning(CS7015): Lec 8.3 True error and Model complexity
  62. 62 Deep Learning(CS7015): Lec 8.4 L2 regularization
  63. 63 Deep Learning(CS7015): Lec 8.5 Dataset augmentation
  64. 64 Deep Learning(CS7015): Lec 8.6 Parameter sharing and tying
  65. 65 Deep Learning(CS7015): Lec 8.7 Adding Noise to the inputs
  66. 66 Deep Learning(CS7015): Lec 8.8 Adding Noise to the outputs
  67. 67 Deep Learning(CS7015): Lec 8.9 Early stopping
  68. 68 Deep Learning(CS7015): Lec 8.10 Ensemble Methods
  69. 69 Deep Learning(CS7015): Lec 8.11 Dropout
  70. 70 Deep Learning(CS7015): Lec 9.1 A quick recap of training deep neural networks
  71. 71 Deep Learning(CS7015): Lec 9.2 Unsupervised pre-training
  72. 72 Deep Learning(CS7015): Lec 9.3 Better activation functions
  73. 73 Deep Learning(CS7015): Lec 9.4 Better initialization strategies
  74. 74 Deep Learning(CS7015): Lec 9.5 Batch Normalization
  75. 75 Deep Learning(CS7015): Lec 10.1 One-hot representations of words
  76. 76 Deep Learning(CS7015): Lec 10.2 Distributed Representations of words
  77. 77 Deep Learning(CS7015): Lec 10.3 SVD for learning word representations
  78. 78 Deep Learning(CS7015): Lec 10.3 (Part-2) SVD for learning word representations (Contd.)
  79. 79 Deep Learning(CS7015): Lec 10.4 Continuous bag of words model
  80. 80 Deep Learning(CS7015): Lec 10.5 Skip-gram model
  81. 81 Deep Learning(CS7015): Lec 10.5 (Part-2) Skip-gram model (Contd.)
  82. 82 Deep Learning(CS7015): Lec 10.6 Contrastive estimation
  83. 83 Deep Learning(CS7015): Lec 10.7 Hierarchical softmax
  84. 84 Deep Learning(CS7015): Lec 10.8 GloVe representations
  85. 85 Deep Learning(CS7015): Lec 10.9 Evaluating word representations
  86. 86 Deep Learning(CS7015): Lec 10.10 Relation between SVD and Word2Vec
  87. 87 Deep Learning(CS7015): Lec 11.1 The convolution operation
  88. 88 Deep Learning(CS7015): Lec 11.2 Relation between input size, output size and filter size
  89. 89 Deep Learning(CS7015): Lec 11.3 Convolutional Neural Networks
  90. 90 Deep Learning(CS7015): Lec 11.3 (Part-2) Convolutional Neural Networks (Contd.)
  91. 91 Deep Learning(CS7015): Lec 11.4 CNNs (success stories on ImageNet)
  92. 92 Deep Learning(CS7015): Lec 11.4 (Par-2) CNNs (success stories on ImageNet) (Contd.)
  93. 93 Deep Learning(CS7015): Lec 11.5 Image Classification continued (GoogLeNet and ResNet)
  94. 94 Deep Learning(CS7015): Lec 12.1 Visualizing patches which maximally activate a neuron
  95. 95 Deep Learning(CS7015): Lec 12.2 Visualizing filters of a CNN
  96. 96 Deep Learning(CS7015): Lec 12.3 Occlusion experiments
  97. 97 Deep Learning(CS7015): Lec 12.4 Finding influence of input pixels using backpropagation
  98. 98 Deep Learning(CS7015): Lec 12.5 Guided Backpropagation
  99. 99 Deep Learning(CS7015): Lec 12.6 Optimization over images
  100. 100 Deep Learning(CS7015): Lec 12.7 Create images from embeddings
  101. 101 Deep Learning(CS7015): Lec 12.8 Deep Dream
  102. 102 Deep Learning(CS7015): Lec 12.9 Deep Art
  103. 103 Deep Learning(CS7015): Lec 12.10 Fooling Deep Convolutional Neural Networks
  104. 104 Deep Learning(CS7015): Lec 13.1 Sequence Learning Problems
  105. 105 Deep Learning(CS7015): Lec 13.2 Recurrent Neural Networks
  106. 106 Deep Learning(CS7015): Lec 13.3 Backpropagation through time
  107. 107 Deep Learning(CS7015): Lec 13.4 The problem of Exploding and Vanishing Gradients
  108. 108 Deep Learning(CS7015): Lec 13.5 Some Gory Details
  109. 109 Deep Learning(CS7015): Lec 14.1 Selective Read, Selective Write, Selective Forget
  110. 110 Deep Learning(CS7015): Lec 14.2 Long Short Term Memory(LSTM) and Gated Recurrent Units(GRUs)
  111. 111 Deep Learning(CS7015): Lec 14.3 How LSTMs avoid the problem of vanishing gradients
  112. 112 Deep Learning(CS7015): Lec 14.3 (Part-2) How LSTMs avoid the problem of vanishing gradients (Contd.)
  113. 113 Deep Learning(CS7015): Lec 15.1 Introduction to Encoder Decoder Models
  114. 114 Deep Learning(CS7015): Lec 15.2 Applications of Encoder Decoder models
  115. 115 Deep Learning(CS7015): Lec 15.3 Attention Mechanism
  116. 116 Deep Learning(CS7015): Lec 15.3 (Part-2) Attention Mechanism (Contd.)
  117. 117 Deep Learning(CS7015): Lec 15.4 Attention over images
  118. 118 Deep Learning(CS7015): Lec 15.5 Hierarchical Attention

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.