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Deep Learning(CS7015): Lec 2.4 Error and Error Surfaces

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Deep Learning(CS7015): Lec 2.4 Error and Error Surfaces

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Deep Learning

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  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

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