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mgelbart authored Nov 13, 2018
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Expand Up @@ -48,10 +48,10 @@ The lecture videos are available [here](https://www.youtube.com/playlist?list=PL
| 32 | Apr 4 | [More CNNs, deep learning software](lectures/L32.pdf?raw=1) | [in-class demo](https://github.com/UBC-CS/cpsc340/blob/master/lectures/L32demo.ipynb), [Artistic Style Transfer video](https://www.youtube.com/watch?v=Uxax5EKg0zA), [Deep Photo Style Transfer](https://github.com/luanfujun/deep-photo-styletransfer), [The Building Blocks of Interpretability (for CNNs)](https://distill.pub/2018/building-blocks/) |
| 33 | Apr 6 | [Conclusion](lectures/L33.pdf?raw=1) | |

Note: the acroynms in the table above refer to the following textbooks:
The acroynms in the table above refer to the following textbooks:

- AI:AMA: Artificial Intelligence: A Modern Approach by Russell and Norvig
- ESL: The Elements of Statistical Learning by Hastie et al.
- ML:APP: Machine Learning: A Probabilistic Perspective by Kevin Murphy
- PRML: Pattern Recognition and Machine Learning by Christopher Bishop
- IDM: Introduction to Data Mining by Steinbach et al.
- AI:AMA: _Artificial Intelligence: A Modern Approach_ by Russell and Norvig
- ESL: _The Elements of Statistical Learning_ by Hastie et al.
- ML:APP: _Machine Learning: A Probabilistic Perspective_ by Kevin Murphy
- PRML: _Pattern Recognition and Machine Learning_ by Christopher Bishop
- IDM: _Introduction to Data Mining_ by Steinbach et al.

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