Recurrent Neural Networks
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01. Introducing Jeremy
02. Section 1: Motivation for RNNs
03. Motivation for RNNs
04. Vanilla supervised learners and structured input
05. Section 2: Motivating and Modelling Recursive Sequences
06. Motivating and modeling recursive sequences
07. Simple recursive examples
08. Recursive or not? Part 1
09. Recursive or not? Part 2
10. Recursive or not? Part 3
11. Ways of thinking about recursivity
12. Driving a recursive sequence
13. Section summary
14. Section 3: Injecting recursivity into a learner (the lazy wa
15. Injecting Recursivity into a Learner (the lazy way)
16. A first example
17. Setting up the example
18. Windowing the example sequence
19. Using Keras for fitting
20. Using a regressor as a generative model
21. A second example
22. Setting up the second example
23. Wrapping up the second example
24. Interesting twists on the second example
25. Real time series example
26. Section summary
27. Section 4: Injecting Recursivity into Learners the Smart Way
28. Coding up a crazy recursive sequence
29. Flaws with the FNN approach
30. RNN fundamental derivations
31. Formulating a Least Squares loss
32. RNNs and memory
33. RNNs and graphical models
34. RNN Technical Issues
35. Section and course summary
36. Outro
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31. Formulating a Least Squares loss
RNNs 4 3 Formulating A Least Squares Loss
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