This is the main website for the Fall 2017 course on Neural Networks and Deep Learning. Please come back for announcements. Lectures: Tuesdays 8:3010:00 room 119 Final exam: since there was no consensus on the exam date, we will have two sessions:  Monday 5.2.2018 14:00  Friday 9.2.2018 14:00 You can come to either of the exam sessions. Makeup exam: Friday Feb 16 at 10:15 in room 202 Last days to hand in projects: JCh: Fri 9.2 1014 AL: Thu 8.2 1014 Extension (only for preauthorised students): Friday Feb 16 at 1214 in room 110 or 202. Grade thresholds for the assignments are: 100 total (60 assignments + 40 project): 90: 5.0, 80: 4.5, 70: 4.0, 60: 3.5, 50: 3.0 Final exam grade thresholds (out of 75 + 10% from assignment): 70: 5.0, 62: 4.5, 53: 4.0, 44: 3.5, 35: 3.0 Assignments and lecture notes: https://github.com/janchorowski/nn_assignments Course rules: course rules.pdf Acknowledgments: I thank Google for awarding the class GCE credits under the Google Cloud Platform Education Grants. 
Teaching > Zimowy 20172018 >
Neural Networks Fall 17
Lectures 1315
During the makeup lecture we have spoken about the SVM and about the PAC learning theory. The slides for PAC are here: https://github.com/janchorowski/nn_assignments/blob/nn17_fall/lecture_notebooks/14PAC.pdf The review slides are here: https://github.com/janchorowski/nn_assignments/blob/nn17_fall/lecture_notebooks/15review.pdf The RL video that I wanted to play to you is here with the accompanying notebook. 
Assignment 6
I've posted assignment #6 which will bring the total exercise points to 60 and adds several bonus questions. Please get it from: https://github.com/janchorowski/nn_assignments/blob/nn17_fall/assignment6/Assignment6.ipynb. Submission deadline:

Makeup lectures
We have selected to host a makeup lecture for Saturday, 27.01.2018. For those that can't make it, please watch lectures 7 & 8 from https://see.stanford.edu/Course/CS229 which cover similar topics. 
Lecture 11
The notebook with kMEANS is at: https://github.com/janchorowski/nn_assignments/blob/nn17_fall/lecture_notebooks/11kMeansSOM.ipynb The additional slides are at: https://github.com/janchorowski/nn_assignments/blob/nn17_fall/lecture_notebooks/11unsupervised.pdf 
Lecture 10
The PCA is explained in the notebook: https://github.com/janchorowski/nn_assignments/blob/nn17_fall/lecture_notebooks/10PCA.ipynb. 
Lecture 09
We have talked about LSTM and RNN usage for real world problems. LSTM demo is in the updated notebook from lecture 8. The attached presentation is from the PLinML meeting, I have also shown it in class. 
Lecture 08
Lecture notebooks are here: Projects Please see the attached slides for information about the projects and reproducibility. If you want to do the reproducibility challenge, please select an article by putting you name into this spreadsheet. Please prepare by the end of next week a 1 page description of the project topics (what is the topic, what resources do you have, what steps do you want to take). Please consult the topic with the instructors. 
Assignment 5
Is posted: https://github.com/janchorowski/nn_assignments/blob/nn17_fall/assignment5/Assignment5.ipynb Since it took us more than predicted to prepare it, the deadline is: Problems 1 and 2 last lab session before or on Wednesday, 13.12.17 Problems 3 and bonuses last lab session before or on Wednesday, 20.12.17 Please note that project descriptions will also be due 20.12. 
Lecture notes 7 & update to HW 4
The notebook with convolutions is here: https://github.com/janchorowski/nn_assignments/blob/nn17_fall/lecture_notebooks/07convolutions.ipynb I decided to exted the deadline for problem 7 (conv net) on HW4 until Wednesday 29.11.17. I have also added a bonus problem, also due 29.11.17. 
Lecture notes 6
The notebook is here: https://github.com/janchorowski/nn_assignments/blob/nn17_fall/lecture_notebooks/06regularizationnumpy.ipynb There's a bounty of 5 exercise points for changing the neural network implmentation to PyTorch. The slides are attached. 
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