https://github.com/fgnt/2019_ad_xidian
https://github.com/fgnt/2019_ad_xidian
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/fgnt/2019_ad_xidian
- Owner: fgnt
- Created: 2019-08-16T14:35:39.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-08-22T08:11:58.000Z (almost 7 years ago)
- Last Synced: 2026-01-28T11:43:49.260Z (4 months ago)
- Language: HTML
- Size: 227 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Introduction
https://fgnt.github.io/2019_ad_xidian/
# Theoretical exercise
https://github.com/fgnt/2019_ad_xidian/blob/master/theory/exercise.pdf
# Practical exercise
Start a jupyter notebook server in the poolroom:
```bash
source /upb/scratch/users/c/cbj/py37/bin/activate
cd ~/ && jupyter notebook
```
For more details see:
https://fgnt.github.io/python_crashkurs_doc/include/poolroom.html
Small numpy introduction: https://fgnt.github.io/python_crashkurs_doc/include/numpy.html
Numpy cheat sheet: https://git.cs.upb.de/chthiel/python-tutorial/blob/master/cheat_sheets/Numpy_Python_Cheat_Sheet.pdf
## Download the exercise:
Download the git repository
```bash
git clone https://github.com/fgnt/2019_ad_xidian.git
```
Now you can find in your home directory a notebook to start the exercise
(`~/2019_ad_xidian/practice/ad_template.ipynb`) and a python script that contains some helper functions (`~/2019_ad_xidian/practice/ad_helper.py`).
Alternative:
Open
https://raw.githubusercontent.com/fgnt/2019_ad_xidian/master/practice/ad_template.ipynb
in a browser and safe the file.
# Final task
Extend the code in jupyter notebook from the practical exercise to a full neuronal network (NN) framework
and train a NN on the MNIST data.
Can you reach 98% accuracy?