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Louis Monier
@louis_monier
https://www.linkedin.com/in/louismonier
Deep Learning for Images
I see what you mean...
Gregory Renard
@redo
https://www.linkedin.com/in/gregoryrenard
Class 2 - Q2 - 2016
Fun with Images
Image Classification: kayak, boy Entity Detection
kayak boy
Face Recognition
Leo
Gollum
More Fun with Images
Pose DetectionImage SegmentationImage Captioning: “A young boy
wearing an orange vest riding a
yellow kayak on water, with sunlight
reflections.”
Yet more Fun with Images
Optical Character Recognition (OCR):
Astronomy is the science which treats of the nature and
properties of the heavenly bodies.
Autonomous Vehicles
Handwriting Recognition:
combustible: “able to catch fire”, adjective for being capable of
igniting and burning.
Our Wet Hardware
Alternating layers of
- simple cells (filters)
- complex cells (combination)
Simple patterns to abstract concepts
~ 5B neurons for vision
Convolutional Neural Network (ConvNet, CNN)
Suggested by Kunihiko Fukushima, 1980
LeNet, by Yann LeCun, 1998, to classify hand-written digits
filterimage
= 6.6
= -7.8
1.0 - really want
0.2 - sort of want
-1.0 - don’t want
Convolution: Applying a Filter to a Signal
through
through
=
=
image filter
1.0
0.5
0.0
through
through
Convolutional Layer - Basic Unit
5x5x3 chunk of inputs
Layer N Layer N+1
ReLU
neuron
(3)
(3)
(3)
(3)
5 x 5 x 3 = 75 inputs
76 weights
Convolutional Layer: Add Depth
5x5x3 chunk of inputs
Layer N Layer N+1
Depth = 7 ReLU neurons in parallel,
with different weights
Stride = 1
Convolutional Layer: Repeat over entire image
L=W=5
D=4
zero
padding
D=7
Shared weights!!!
Pooling Layer: Squeeeeeze!
Max Pooling Average Pooling
Layer N+1Layer N
Classical CNN topology - VGGNet (2013)
224x224 112x112 56x56 28x28 14x14 FC
D=64
D=128
D=256
D=512
D=512
D=4096 D=4096 D=1000
FC FC + Softmax
ConvNet
Pool
Layer 1
Filter Matching images
Layer 2
Layer 3
Layer 4
Layer 5
Modern ConvNet - GoogLeNet
GoogLeNet (2014)
ResNet-34 (2015)
Manifolds
Real-life Data vs Random Data
If music be the food of love, play on!
-- William Shakespeare
3Flr'kI5;LS3oLj1xK52,BA1 Rea5IYSf
-- 1000 monkeys typing
-- Real world -- Random Pixels
Deep Learning Class #2 - Deep learning for Images, I See What You Mean
Workshop : Keras & MNIST
https://github.com/holbertonschool/deep-learning/tree/master/Class%20%232
Workshop : Keras & CIFAR 10
https://github.com/holbertonschool/deep-learning/tree/master/Class%20%232

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Deep Learning Class #2 - Deep learning for Images, I See What You Mean

  • 1. Louis Monier @louis_monier https://www.linkedin.com/in/louismonier Deep Learning for Images I see what you mean... Gregory Renard @redo https://www.linkedin.com/in/gregoryrenard Class 2 - Q2 - 2016
  • 2. Fun with Images Image Classification: kayak, boy Entity Detection kayak boy Face Recognition Leo Gollum
  • 3. More Fun with Images Pose DetectionImage SegmentationImage Captioning: “A young boy wearing an orange vest riding a yellow kayak on water, with sunlight reflections.”
  • 4. Yet more Fun with Images Optical Character Recognition (OCR): Astronomy is the science which treats of the nature and properties of the heavenly bodies. Autonomous Vehicles Handwriting Recognition: combustible: “able to catch fire”, adjective for being capable of igniting and burning.
  • 5. Our Wet Hardware Alternating layers of - simple cells (filters) - complex cells (combination) Simple patterns to abstract concepts ~ 5B neurons for vision
  • 6. Convolutional Neural Network (ConvNet, CNN) Suggested by Kunihiko Fukushima, 1980 LeNet, by Yann LeCun, 1998, to classify hand-written digits
  • 7. filterimage = 6.6 = -7.8 1.0 - really want 0.2 - sort of want -1.0 - don’t want Convolution: Applying a Filter to a Signal through through = = image filter 1.0 0.5 0.0 through through
  • 8. Convolutional Layer - Basic Unit 5x5x3 chunk of inputs Layer N Layer N+1 ReLU neuron (3) (3) (3) (3) 5 x 5 x 3 = 75 inputs 76 weights
  • 9. Convolutional Layer: Add Depth 5x5x3 chunk of inputs Layer N Layer N+1 Depth = 7 ReLU neurons in parallel, with different weights
  • 10. Stride = 1 Convolutional Layer: Repeat over entire image L=W=5 D=4 zero padding D=7 Shared weights!!!
  • 11. Pooling Layer: Squeeeeeze! Max Pooling Average Pooling Layer N+1Layer N
  • 12. Classical CNN topology - VGGNet (2013) 224x224 112x112 56x56 28x28 14x14 FC D=64 D=128 D=256 D=512 D=512 D=4096 D=4096 D=1000 FC FC + Softmax ConvNet Pool
  • 18. Modern ConvNet - GoogLeNet GoogLeNet (2014) ResNet-34 (2015)
  • 20. Real-life Data vs Random Data If music be the food of love, play on! -- William Shakespeare 3Flr'kI5;LS3oLj1xK52,BA1 Rea5IYSf -- 1000 monkeys typing -- Real world -- Random Pixels
  • 22. Workshop : Keras & MNIST https://github.com/holbertonschool/deep-learning/tree/master/Class%20%232
  • 23. Workshop : Keras & CIFAR 10 https://github.com/holbertonschool/deep-learning/tree/master/Class%20%232