Slides for ACT-W workshop (http://act-wsea.splashthat.com/)

1. INTRODUCTIONTO
DEEP LEARNING
Zeynep Su Kurultay

2. Outline
■ Modeling humans in machines
■ Introduction to neural nets
■ What makes an algorithm intelligent?
■ Learning
– Supervised learning
■ Deep learning
– Neural nets in detail
■ Demo and use case
■ Future

3. Demo environment
■ Download and install Python 2 &Theano
■ http://bit.ly/1Hj3pig

4. Modeling humans in machines

5. Modeling humans in machines
But why?

6. Neural networks
■ The mammal brain is organized in a deep
architecture (Serre, Kreiman, Kouh, Cadieu,
Knoblich, & Poggio, 2007)
(E.g. visual system has 5 to 10 levels)
■ Very popular at the beginning of 1990s but fell
out of favor after it was found that they were
not performing well
■ Why is it gaining power again now: Deep
architectures might be able to represent some
functions otherwise not efficiently
representable. Breakthrough in 2006/2007 with
Hinton, Bengio papers

7. Examples around us

8. Examples around us
Date: November 2014

9. Examples around us

10. Examples around us

11. Examples around us

12. What makes an algorithm intelligent?
Image courtesy ofToptal.com

13. What makes an algorithm intelligent?

14. What makes an algorithm intelligent?

15. What makes an algorithm intelligent?

16. Learning
■ Supervised machine learning:The program is “trained” on a pre-defined set of
“training examples”, which then facilitate its ability to reach an accurate conclusion
when given new data.
■ Semi-supervised machine learning:The program infers the unknown labels through
“label propagation”, utilizing similarities between different examples and inferring
non-existent labels from existent ones
■ Unsupervised machine learning:The program is given a bunch of data and must find
patterns and relationships therein. – e.g. clustering via nearest neighbor algorithm

17. Supervised Learning
■ Binary classification: Does this person have that disease?
■ Regression:What is the market value of this house?
■ Multiclass classification: Digit recognition, Face recognition

18. Supervised Learning
■ Goal: Given a number of features, try to make sense out of it!
■ Example: Employee satisfaction rates – depends on ?  So, given these
features in a dataset, try to predict the rate

19. Supervised Learning

20. Supervised Learning

21. Supervised Learning

22. Supervised Learning

23. Supervised Learning
■ But how do we adjust ourselves? How do we know at each step we are getting better?
■ Measurement of wrongness: Loss functions

24. Loss functions

25. Gradient descent
How do we know how to “roll down
the hill”?
The gradient (the derivatives of the
loss function over all of the individual
weights of features -i.e. parameters-)
tells us “which way is down”.

26. What exactly is deep learning?
■ “a network would need more than one hidden layer to be a deep network, networks
with one or two hidden layers are traditional neural networks…….”
■ “in my experience, a network can be considered deep when there is at least one
hidden layer.Although the term deep learning can be fuzzy, …”
■ “in my own thinking, deep is not related to the number of layers, but it talks about
how hard the feature to be discovered is…….”
■ - a discussion from StackExchange

27. Deep learning
■ What is the difference? Remember the quote fromYann LeCun from before? It goes
on:
■ “A pattern recognition system is like a black box with a camera at one end, a green
light and a red light on top, and a whole bunch of knobs on the front…. Now, imagine a
box with 500 million knobs, 1,000 light bulbs, and 10 million images to train it with.
That’s what a typical Deep Learning system is.”

29. Aim: Learning features
■ Deep learning excels in tasks where the basic unit, a single
pixel, a single frequency, or a single word has very little
meaning in and of itself, but the combination of such units
has a useful meaning. It can learn these useful combinations
of values without any human intervention.

30. Aim: Learning features

31. Neural networks
■ An input, output, and one or more hidden layers
of units/neurons/perceptrons
■ Each connection between two neurons has a
weight w (similar to the perceptron weights).
Best weights can again be found with gradient
descent.
Image courtesy of
http://ljs.academicdirect.org/A15/053_070.htm

32. Neural networks
■ Example: Input vector: [7, 1, 2]  Into the input
units
■ These values are then propagated forward to the
hidden units using the weighted sum transfer
function for each hidden unit - forward
propagation -, which in turn calculate their
outputs - activation function -.
Image courtesy of
http://ljs.academicdirect.org/A15/053_070.htm

33. Neural networks
■ Why deep?
■ Number of parameterized transformations a
signal encounters as it propagates from the
input layer to the output layer, where a
parameterized transformation is a processing
unit that has trainable parameters, such as
weights.
Image courtesy of
http://ljs.academicdirect.org/A15/053_070.htm

34. Aim: Learning features
■ The goal of deep learning methods is to learn higher
levels of feature from lower level features.

35. Notes for Demo
■ Overfitting – there is such a thing as learning too much –or too specific-!
■ Regularization – a technique that prevents overfitting

36. Notes for Demo
■ Overfitting – there is such a thing as learning too much –or too specific-!
■ Regularization – a technique that prevents overfitting

37. Notes for Demo
U.S. Census Population overTime

38. Notes for Demo
We will be using MNIST digits dataset
that serves as a benchmark to
compare results with as new articles
come out.

39. Last output

40. Demo
Code snippets – inside the gradient descent
Output =Wx+b

41. Demo
Code snippets – inside the hidden layer

42. Demo
Code snippets – inside the hidden layer

43. Demo
Code snippets – inside the hidden layer

44. Demo
Code snippets – inside the network

45. Demo
■ https://algorithmia.com/demo/handwriting

46. Future of deep learning
■ Deep learning has a lot of hype right now, and it is apparent that it is very useful for
specific tasks.
■ What frontiers and challenges do you think are the most exciting for researchers in
the field of neural networks in the next ten years?
■ I cannot see ten years into the future. For me, the wall of fog starts at about 5 years. ...
I think that the most exciting areas over the next five years will be really understanding
videos and text. I will be disappointed if in five years time we do not have something
that can watch aYouTube video and tell a story about what happened. I have had a lot
of disappointments.
– From Geoffrey Hinton’s AMA on Reddit

47. Now &The future
Facebook Deep Learning, March 26, 2015
Image courtesy ofVenturebeat.com

48. Further resources
■ Introductory:
■ Andrew Ng’s Machine Learning course on Coursera
■ Geoffrey Hinton’s Neural Networks course on Coursera
■ Advanced:
■ Who is afraid of non-convex loss functions? ByYann LeCun http://videolectures.net/eml07_lecun_wia/
■ For those who like papers, recent advances:
■ Playing Atari with Deep Reinforcement Learning - http://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
■ Unsupervised Face Detection - http://cs.stanford.edu/~quocle/faces_full.pdf

49. ■ Content:
■ Toptal.com, Deeplearning.net
■ http://www.computerworld.com/article/2918161/emerging-technology/the-ai-ecosystem.html
■ Introduction to Machine Learning CMU-10701 - Deep Learning slides
■ Images:
■ http://www.spyemporium.com/images/products/st-sc1720.jpg
■ http://stats.stackexchange.com/questions/128616/whats-a-real-world-example-of-overfitting
■ http://www.homedepot.com/catalog/productImages/1000/c4/c4c34d2e-56ce-4c11-94c0-67aa19b769fa_1000.jpg
■ http://www.bulborama.com/images/products/1933.jpg
■ https://xkcd.com/1122/, https://xkcd.com/1425/