Machine Learning 2

1. Machine Learning
Deep Learning
Inas A. Yassine
Systems and Biomedical Engineering Department,
Faculty of Engineering - Cairo University
iyassine@eng.cu.edu.eg

2. Self-taught learning
Testing:
What is this?
Car Motorcycle
Unlabeled images (random internet images)

3. Deep Learning
§ Biology Aspect
§ Each neuron is fired due to a certain edge
direction
§ New Wiring Experiment
§ Brain port
§ Automate what we see as a face….

4. Self-taught learning
Sparse
coding,
LCC, etc.
f1, f2, …, fk
Car Motorcycle
Use learned f1, f2, …, fk to represent training/test sets.
Using f1, f2, …, fk
a1, a2, …, ak
If have labeled training
set is small, can give
huge performance
boost.

5. Learning feature
hierarchies/Deep learning

6. Why feature hierarchies
pixels edges object parts
(combination
of edges)
Convolution batches !

7. Deep learning algorithms
§ Stack sparse coding algorithm
§ Deep Belief Network (DBN) (Hinton)
§ Deep sparse autoencoders (Bengio)
§ Deep Convolution Neural Networks
§ Residual Networks
§ Seams Networks
§ Self Learning Netowrks
[Other related work: LeCun, Lee,Yuille, Ng …]

8. Deep Learning:Autoencoder

9. Deep learning with autoencoders
§ Logistic regression
§ Neural network
§ Sparse autoencoder
§ Deep autoencoder

10. Logistic regression has a learned parameter vector q.
On input x, it outputs:
where
Logistic regression
x1
x2
x3
+1
Draw a logistic
regression unit as:

11. Neural Network
String a lot of logistic units together. Example 3 layer network:
x1
x2
x3
+1 +1
a3
a2
a1
Layer 1 Layer 2
Layer 3

12. Neural Network
x1
x2
x3
+1 +1
Layer 1 Layer 2
Layer 4+1
Layer 3
Example” 4 layer network with 2 output units:

13. Training a neural network
Given training set (x1, y1), (x2, y2), (x3, y3 ), ….
Adjust parameters q (for every node) to make:
(Use gradient descent.“Backpropagation” algorithm. Susceptible to local optima.)

14. Unsupervised feature learning
x4
x5
x6
+1
Layer 1
Layer 2
x1
x2
x3
x4
x5
x6
x1
x2
x3
+1
Layer 3
Network is trained to
output the input (learn
identify function).
Minimizing both information
of data and output
Trivial solution unless:
- Constrain number of units
in Layer 2 (learn compressed
representation), or
- Constrain Layer 2 to be
sparse.
a1
a2
a3

15. Training a sparse autoencoder.
Given unlabeled training set x1, x2,
Unsupervised feature learning with ANN
Reconstruction error
term
𝑊" 𝑊X
a1
a2
a3

16. Unsupervised feature learning with ANN
x4
x5
x6
+1
Layer 1
Layer 2
x1
x2
x3
x4
x5
x6
x1
x2
x3
+1
Layer 3

17. Unsupervised feature learning with ANN
New representation for input.
x4
x5
x6
+1
Layer 1
Layer 2
x1
x2
x3
+1

18. Unsupervised feature learning with ANN
x4
x5
x6
+1
Layer 1
Layer 2
x1
x2
x3
+1
+1
b1
b2
b3
Train parameters so that ,
subject to bi’s being sparse.

19. Greedy Learning
Regularization
using back
propagation of
the complete
system after
greedy + 5%
increase in
performance
x4
x5
x6
+1
Layer 1
Layer 2
x1
x2
x3
+1+1
b1
b2
b3
x4
x5
x6
+1
Layer 1
Layer 2
x1
x2
x3
+1

20. Sparse Autoencoder

21. First stage of visual processing in
brain:V1
Schematic of simple cell Actual simple cell
“Gabor functions.”
The first stage of
visual processing in
the brain (V1) does
“edge detection.”

22. Learning an image representation
Sparse coding (Olshausen & Field,1996)
Input: Images x(1), x(2), …, x(m) (each in Rn x n)
Learn: Dictionary of bases f1, f2, …, fk (also Rn x n), so that each
input x can be approximately decomposed as:
s.t. aj’s are mostly zero (“sparse”)
Use to represent 14x14 image patch succinctly, as [a7=0.8, a36=0.3,
a41 = 0.5]. I.e., this indicates which “basic edges” make up the
image.

23. Sparse coding illustration
Natural Images
Learned bases (f1 , …, f64): “Edges”
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» 0.8 * + 0.3 * + 0.5 *
x » 0.8 * f36
+ 0.3 * f42 + 0.5 * f63
[0, 0, …, 0, 0.8, 0, …, 0, 0.3, 0, …, 0, 0.5, …]
Test example

24. Represent as: [0, 0, …, 0, 0.6, 0, …, 0, 0.8, 0, …, 0, 0.4, …]
Represent as: [0, 0, …, 0, 1.3, 0, …, 0, 0.9, 0, …, 0, 0.3, …]
More examples
» 0.6 * + 0.8 * + 0.4 *
f15 f28
f37
» 1.3 * + 0.9 * + 0.3 *
f5 f18
f29
• Method hypothesizes that edge-like patches are the most
“basic” elements of a scene, and represents an image in terms of
the edges that appear in it.
• Use to obtain a more compact, higher-level representation of
the scene than pixels.

25. Sparse Learning
§ Input: Images x(1), x(2), …, x(m) (each in Rn x
n)
Reconstruction error
term
𝑊" 𝑊X
Regularization objective :
• Small?
• Too much energy to be fired
• Different neurons
• L1 norm
• ΞΞ |W X|

26. DEEP LEARNING:
CONVOLUTION NEURAL
NETWORK

27. ConvNets (Fukushima, LeCun,
Hinton)

28. ConvNets

30. Convolution
§ Correlation
§ Convolution

31. Image Convolution

33. Image Convolution

34. Image Convolution

35. Image Convolution

36. ConvNets in Torch