Recurrent neural networks (RNNs) are designed to model sequential information and are widely used to solve the problems of speech recognition, language modeling, translation, and image captioning. In this webinar, Rajiv Shah, Adjunct Assistant Professor at University of Illinois, will provide a brief introduction to recurrent neural networks. Learn how recurrent neural networks operate. Walk through code in TensorFlow for modeling a sine wave, performing basic addition, and generating handwriting.

1. August 31, 2016
Rajiv shah
RajivShah.com
rshahATpobox.com
github.com/rajshah4/
rajcs4
Teaching
Recurrent Neural Networks
using Tensorflow

2. • why
• theory/app - deep learning
• theory/app - recurrent neural
networks

4. http://googleresearch.blogspot.com/2015/11/computer-respond-to-this-email.html
smart reply

5. https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html
text summarization

6. http://blog.kaggle.com/2016/01/04/how-much-did-it-rain-ii-winners-interview-1st-place-pupa-aka-aaron-sim/
kaggle

7. https://magenta.tensorflow.org/2016/07/15/lookback-rnn-attention-rnn/
music

8. theory:
deep learning

9. http://playground.tensorflow.org/
feature rich model

10. http://playground.tensorflow.org/
deep learning model

11. http://cs231n.github.io/neural-networks-1/
deep learning

12. theory:
operation

13. Recurrent
recurrent neural network

14. http://colah.github.io/posts/2015-08-Understanding-LSTMs/
rnn: rolled out
10 22310+3+22

15. demo:
char-rnn
http://cs.stanford.edu/people/karpathy/recurrentjs/

16. theory ii:
cells

17. http://colah.github.io/posts/2015-08-Understanding-LSTMs/
rnn cell

18. vanish gradients

19. http://googleresearch.blogspot.com/2015/11/computer-respond-to-this-email.html

20. http://colah.github.io/posts/2015-08-Understanding-LSTMs/
long short term
memory (LSTM)

21. long short term memory

22. https://github.com/rajshah4/tensorflow_shiny
demo: sine wave

23. theory iii:
architecture

24. rnn architecture

25. rnn architecture:
one to many

26. rnn architecture:
one to many

27. rnn architecture:
many to many

28. (array([[7],
[4],
[1],
[8],
[0],
[3],
[1],
[5],
[6],
[9],
[2],
[0],
[0],
[0],
[0]]), 46.0)
demo: addition
http://projects.rajivshah.com/blog/2016/04/05/rnn_addition/

29. generative

30. demo: basketball trajectories
http://tinyurl.com/traj-rnn

31. demo: handwriting
http://www.cs.toronto.edu/~graves/handwriting.html

32. • rnn’s have value
• lots of good examples you can use as
a starting point in building an rnn

33. https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html
text summarization

34. August 31, 2016
Rajiv shah
RajivShah.com
rshahATpobox.com
github.com/rajshah4/
rajcs4
Teaching
Recurrent Neural Networks
using Tensorflow