https://telecombcn-dl.github.io/2017-dlsl/ Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN. The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.

1. Day 2 Lecture 1
Deep Belief Networks
(DBN)
Elisa Sayrol

2. Restrictive Boltzmann Machine (RBM)
Training. Contrastive Divergence (CD)
Deep Belief Networks (DBN)
Overview

3. 3
Restricted Boltzmann Machine (RBM)
Figure: Geoffrey Hinton (2013)
Salakhutdinov, Ruslan, Andriy Mnih, and Geoffrey Hinton. "Restricted Boltzmann machines for
collaborative filtering." Proceedings of the 24th international conference on Machine learning. ACM, 2007.
● Shallow two-layer net.
● Restricted=No two nodes in a layer share a
connection
● Bipartite graph.
● Bidirectional graph
○ Shared weights.
○ Different biases.

4. 4
Restricted Boltzmann Machine (RBM)
Figure: Geoffrey Hinton (2013)
Salakhutdinov, Ruslan, Andriy Mnih, and Geoffrey Hinton. "Restricted Boltzmann machines for
collaborative filtering." Proceedings of the 24th international conference on Machine learning. ACM, 2007.

5. 5
Restricted Boltzmann Machine (RBM)
DeepLearning4j, “A Beginner’s Tutorial for Restricted Boltzmann Machines”.
Forward
pass

6. 6
Restricted Boltzmann Machine (RBM)
DeepLearning4j, “A Beginner’s Tutorial for Restricted Boltzmann Machines”.
Backward
pass
c
c
c
c

7. 7
Restricted Boltzmann Machine (RBM)
DeepLearning4j, “A Beginner’s Tutorial for Restricted Boltzmann Machines”.
Backward
pass
The reconstructed values
at the visible layer are
compared with the actual
ones with the KL
Divergence.

8. 8
What are the Maths behind RBMs?
(Estimation of the parameters)
Geoffrey Hinton, "Introduction to Deep Learning & Deep Belief Nets” (2012)
Geoorey Hinton, “Tutorial on Deep Belief Networks”. NIPS 2007.

9. 9
What are the Maths behind RBMs?
Other references:
Deeplearning.net: Restricted Boltzmann Machines (with Theano functions and concepts)
Hugo Larochelle: Course on NN
Let’s take a look at some of his slides on RBM….

10. 10
What are the Maths behind RBMs?
Hugo Larochelle Slides

11. Hugo Larochelle Slides

12. Hugo Larochelle Slides

13. Hugo Larochelle Slides

14. Hugo Larochelle Slides

15. Hugo Larochelle Slides

16. Hugo Larochelle Slides

17. Hugo Larochelle Slides

18. Hugo Larochelle Slides

19. 19
Deep Belief Networks (DBN)
Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief
nets." Neural computation 18, no. 7 (2006): 1527-1554.
● Architecture like an MLP.
● Training as a stack of
RBMs.

20. 20
Deep Belief Networks (DBN)
Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief
nets." Neural computation 18, no. 7 (2006): 1527-1554.
● Architecture like an MLP.
● Training as a stack of
RBMs.

21. 21
Deep Belief Networks (DBN)
Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief
nets." Neural computation 18, no. 7 (2006): 1527-1554.
● Architecture like an MLP.
● Training as a stack of
RBMs.

22. 22
Deep Belief Networks (DBN)
Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief
nets." Neural computation 18, no. 7 (2006): 1527-1554.
● Architecture like an MLP.
● Training as a stack of
RBMs.

23. 23
Deep Belief Networks (DBN)
Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief
nets." Neural computation 18, no. 7 (2006): 1527-1554.
● Architecture like an MLP.
● Training as a stack of
RBMs…
● ...so they do not need
labels:
Unsupervised
learning

24. 24
Deep Belief Networks (DBN)
Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief
nets." Neural computation 18, no. 7 (2006): 1527-1554.
After the DBN is trained, it can
be fine-tuned with a reduced
amount of labels to solve a
supervised task with superior
performance.
Supervised
learning
Softmax

25. Thank You!