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Intro to Deep Learning Slides Credit: Andrej Karapathy, Derek Hoiem, Marc Aurelio, Yann LeCunn Why this class? Deep Features Have been able to harness the big data in the most efficient and effective manner. Lead to several state of the

  1. Intro to Deep Learning Slides Credit: Andrej Karapathy, Derek Hoiem, Marc Aurelio, Yann LeCunn

  2. Why this class?

  7. Deep Features • Have been able to harness the big data in the most efficient and effective manner. • Lead to several state of the art results!

  9. r o r r E % 5 < : e c n a m r o f r e P 5 1 0 2

  14. What is deep learning?

  19. Space of Visual Features: Non Linear

  29. Deep Neural Networks

  56. Convolution Layer Filters always extend the full depth of the input volume 32x32x3 image 5x5x3 filter 32 Convolve the filter with the image i.e. “slide over the image spatially, computing dot products” 32 3 Lecture 7 - Lecture 7 - 27 Jan 2016 27 Jan 2016 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson 12 56

  57. Convolution Layer 32x32x3 image 5x5x3 filter 32 1 number: the result of taking a dot product between the filter and a small 5x5x3 chunk of the image 32 (i.e. 5*5*3 = 75-dimensional dot product + bias) 3 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 13 27 Jan 2016 27 Jan 2016 57

  58. Convolution Layer activation map 32x32x3 image 5x5x3 filter 32 28 convolve (slide) over all spatial locations 28 32 3 1 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 14 27 Jan 2016 27 Jan 2016 58

  62. Closer look at Spatial Dimensions A closer look at spatial dimensions: 7 7x7 input (spatially) assume 3x3 filter 7 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 27 Jan 2016 27 Jan 2016 24 62

  63. Closer look at Spatial Dimensions A closer look at spatial dimensions: 7 7x7 input (spatially) assume 3x3 filter 7 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 25 27 Jan 2016 27 Jan 2016 63

  64. Closer look at Spatial Dimensions A closer look at spatial dimensions: 7 7x7 input (spatially) assume 3x3 filter 7 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 27 Jan 2016 27 Jan 2016 26 64

  65. Closer look at Spatial Dimensions A closer look at spatial dimensions: 7 7x7 input (spatially) assume 3x3 filter => 5x5 output 7 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 27 Jan 2016 27 Jan 2016 28 65

  66. Closer look at Spatial Dimensions A closer look at spatial dimensions: 7 7x7 input (spatially) assume 3x3 filter applied with stride 2 7 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 27 Jan 2016 27 Jan 2016 29 66

  67. Closer look at Spatial Dimensions A closer look at spatial dimensions: 7 7x7 input (spatially) assume 3x3 filter applied with stride 2 7 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 30 27 Jan 2016 27 Jan 2016 67

  68. Closer look at Spatial Dimensions A closer look at spatial dimensions: 7 7x7 input (spatially) assume 3x3 filter applied with stride 2 => 3x3 output! 7 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 31 27 Jan 2016 27 Jan 2016 68

  69. Closer look at Spatial Dimensions A closer look at spatial dimensions: 7 7x7 input (spatially) assume 3x3 filter applied with stride 3? doesn’t fit! 7 cannot apply 3x3 filter on 7x7 input with stride 3. Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 27 Jan 2016 27 Jan 2016 33 69

  70. N Output size: (N - F) / stride + 1 F e.g. N = 7, F = 3: N F stride 1 => (7 - 3)/1 + 1 = 5 stride 2 => (7 - 3)/2 + 1 = 3 stride 3 => (7 - 3)/3 + 1 = 2.33 :\ Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 27 Jan 2016 27 Jan 2016 34 70

  71. In practice: Common to zero pad the border 0 0 0 0 0 0 e.g. input 7x7 3x3 filter, applied with stride 1 0 pad with 1 pixel border => what is the output? 0 0 0 (recall:) (N - F) / stride + 1 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 35 27 Jan 2016 27 Jan 2016 71

  72. Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 43 27 Jan 2016 27 Jan 2016 72

  73. Examples time: Input volume: 32x32x3 10 5x5 filters with stride 1, pad 2 Output volume size: ? Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 39 27 Jan 2016 27 Jan 2016 73

  74. Examples time: Input volume: 32x32x3 10 5x5 filters with stride 1, pad 2 Output volume size: (32+2*2-5)/1+1 = 32 spatially, so 32x32x10 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 40 27 Jan 2016 27 Jan 2016 74

  75. Examples time: Input volume: 32x32x3 10 5x5 filters with stride 1, pad 2 Number of parameters in this layer? Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 41 27 Jan 2016 27 Jan 2016 75

  76. Examples time: Input volume: 32x32x3 10 5x5 filters with stride 1, pad 2 Number of parameters in this layer? each filter has 5*5*3 + 1 = 76 params (+1 for bias) => 76*10 = 760 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 27 Jan 2016 27 Jan 2016 42 76

  77. Fully Connected Layer Connects to entire input volume similar to DNN 77

  78. Fully Connected Layer Number of parameters: 10x10x10 connected to 1000 FC 1000 FC to 1000 FC. 78

  79. Special Layers • Pooling • Contrast Normalization

  80. Pooling layer - makes the representations smaller and more manageable - operates over each activation map independently: Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 54 27 Jan 2016 27 Jan 2016 80

  82. Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 27 Jan 2016 27 Jan 2016 82 56

  83. Local Response Normalization 83

  84. Example Architectures 84

  85. Case Study: LeNet-5 [LeCun et al., 1998] Conv filters were 5x5, applied at stride 1 Subsampling (Pooling) layers were 2x2 applied at stride 2 i.e. architecture is [CONV-POOL-CONV-POOL-CONV-FC] Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 60 27 Jan 2016 27 Jan 2016 85

  86. Imagenet Network

  87. Case Study: AlexNet [Krizhevsky et al. 2012] Input: 227x227x3 images First layer (CONV1): 96 11x11 filters applied at stride 4 => Q: what is the output volume size? Hint: (227-11)/4+1 = 55 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 27 Jan 2016 27 Jan 2016 61 87

  88. Case Study: AlexNet [Krizhevsky et al. 2012] Input: 227x227x3 images First layer (CONV1): 96 11x11 filters applied at stride 4 => Output volume [55x55x96] Q: What is the total number of parameters in this layer? Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - Lecture 7 - 62 27 Jan 2016 27 Jan 2016 88

  89. Case Study: AlexNet [Krizhevsky et al. 2012] Input: 227x227x3 images First layer (CONV1): 96 11x11 filters applied at stride 4 => Output volume [55x55x96] Parameters: (11*11*3)*96 = 35K Lecture 7 - Lecture 7 - 27 Jan 2016 27 Jan 2016 Fei-Fei Li & Andrej Karpathy & Justin Johnson Fei-Fei Li & Andrej Karpathy & Justin Johnson 63 89

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