Slides by Louis Monier (Altavista Co-Founder & CTO) for Deep Learning keynote #2 at Holberton School. http://www.meetup.com/Holberton-School/events/230547621/ The keynote was followed by a workshop prepared by Gregory Renard. If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/

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

13. Layer 1
Filter Matching images

14. Layer 2

15. Layer 3

16. Layer 4

17. Layer 5

18. Modern ConvNet - GoogLeNet
GoogLeNet (2014)
ResNet-34 (2015)

19. Manifolds

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