https://telecombcn-dl.github.io/2017-dlcv/ Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.

1. [course site]
#DLUPC
Kevin McGuinness
kevin.mcguinness@dcu.ie
Research Fellow
Insight Centre for Data Analytics
Dublin City University
Perceptrons
Day 1 Lecture 2

2. 2
Outline
1. Supervised learning: regression/classification
2. Single neuron models (perceptrons)
a. Linear regression
b. Logistic regression
c. Multiple outputs and softmax regression
3. Multi-layered perceptrons

3. Types of machine learning
We can categorize three types of learning procedures:
1. Supervised Learning:
= ƒ( )
2. Unsupervised Learning:
ƒ( )
3. Reinforcement Learning:
= ƒ( )
We have a labeled dataset with pairs (x, y), e.g.
classify a signal window as containing speech or not:
x1
= [x(1), x(2), …, x(T)] y1
= “no”
x2
= [x(T+1), …, x(2T)] y2
= “yes”
x3
= [x(2T+1), …, x(3T)] y3
= “yes”
...

4. Supervised learning
Fit a function: = ƒ( ), ∈ ℝm
Given paired training examples {(xi
, yi
)}
Key point: generalize well to unseen examples
Depending on the type of target we get:
● Regression: ∈ ℝN
is continuous (e.g. temperatures = {19º, 23º, 22º})
● Classification: is discrete (e.g. = {1, 2, 5, 2, 2}).
○ Beware! These are unordered categories, not numerically meaningful
outputs: e.g. code[1] = “dog”, code[2] = “cat”, code[5] = “ostrich”, ...

5. Black box abstraction of supervised learning

6. Regression

7. Regression example

8. Classification

9. Classification example

10. Single neuron model (perceptron)

11. Biological inspiration

12. Biological inspiration

13. Single neuron model (perceptron)

14. Ingredients

15. Linear regression

16. Logistic regression

17. Multiple outputs

18. Multiple classes

19. Multi-class classification with softmax regression
The softmax activation function is the analogue
of the sigmoid for more than two classes
Max-likelihood loss function is categorical cross
entropy

20. Effect of the softmax

21. Non-linear decision boundaries
Perceptrons can only produce linear decision
boundaries.
Many interesting problems are not linearly
separable.
Real world problems often need non-linear
boundaries
● Images
● Audio
● Text

22. Multi layer perceptrons

23. Deep learning vs shallow learning
● Old style machine learning:
○ Engineer features (by some unspecified method)
○ Create a representation (descriptor)
○ Train shallow classifier on representation
● Example:
○ SIFT features (engineered)
○ BoW representation (engineered + unsupervised
learning)
○ SVM classifier (convex optimization)
● Deep learning
○ Learn layers of features, representation, and
classifier in one go based on the data alone
○ Primary methodology: deep neural networks
(non-convex)
Data
Pixels
Deep learning
Convolutional Neural
Network
Data
Pixels
Low-level
features
SIFT
Representation
BoW/VLAD/Fisher
Classifier
SVM
EngineeredUnsupervisedSupervised
Supervised
23

24. Example: feature engineering in computer vision
24
ϕ(x)

25. Deep nets
Just a neural network with several hidden layers
● Often uses different types of layers, especially
fully connected, convolution, and pooling layers
● Output of each layer can be thought of as a
representation of the input
● Outputs of the lower layers are more closely
related to the input features
● Outputs of the higher layers contain more
abstract features closer in semantics to the
target variable
25
x1
x2
x3
x4
y1
Layer 1
Layer 2
Layer 3
Layer 0
y2

26. Questions?