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https://github.com/hunkim/deeplearningzerotoall
TensorFlow Basic Tutorial Labs
https://github.com/hunkim/deeplearningzerotoall
keras lab mxnet python pytorch tensorflow tutorial
Last synced: 5 days ago
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TensorFlow Basic Tutorial Labs
- Host: GitHub
- URL: https://github.com/hunkim/deeplearningzerotoall
- Owner: hunkim
- Created: 2017-02-20T06:26:23.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2023-10-31T15:04:17.000Z (about 1 year ago)
- Last Synced: 2025-01-02T00:06:33.418Z (20 days ago)
- Topics: keras, lab, mxnet, python, pytorch, tensorflow, tutorial
- Language: Jupyter Notebook
- Homepage: https://www.youtube.com/user/hunkims
- Size: 731 KB
- Stars: 4,484
- Watchers: 422
- Forks: 2,672
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
Awesome Lists containing this project
README
# Lab code (WIP), but call for comments
[](https://travis-ci.org/hunkim/DeepLearningZeroToAll)
This is code for labs covered in TensorFlow basic tutorials (in Korean) at https://youtu.be/BS6O0zOGX4E.
(We also have a plan to record videos in English.)
This is work in progress, and may have bugs.
However, we call for your comments and pull requests. Check out our style guide line:
* More TF (1.0) style: use more recent and decent TF APIs.
* More Pythonic: fully leverage the power of python
* Readability (over efficiency): Since it's for instruction purposes, we prefer *readability* over others.
* Understandability (over everything): Understanding TF key concepts is the main goal of this code.
* KISS: Keep It Simple Stupid! https://www.techopedia.com/definition/20262/keep-it-simple-stupid-principle-kiss-principle
## Lab slides:
* https://goo.gl/jPtWNt
We welcome your comments on slides.
## File naming rule:
* klab-XX-X-[name].py: Keras labs code
* lab-XX-X-[name].py: TensorFlow lab code
* mxlab-XX-X-[name].py: MXNet lab code
## Install requirements
```bash
pip install -r requirements.txt
```
## Run test and autopep8
TODO: Need to add more test cases
```bash
python -m unittest discover -s tests;
# http://stackoverflow.com/questions/14328406/
pip install autopep8 # if you haven't install
autopep8 . --recursive --in-place --pep8-passes 2000 --verbose
```
## Automatically create requirements.txt
```bash
pip install pipreqs
pipreqs /path/to/project
```
http://stackoverflow.com/questions/31684375
## Contributions/Comments
We always welcome your comments and pull requests.
## Reference Implementations
* https://github.com/nlintz/TensorFlow-Tutorials/
* https://github.com/golbin/TensorFlow-ML-Exercises
* https://github.com/FuZer/Study_TensorFlow
* https://github.com/fchollet/keras/tree/master/examples