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https://github.com/taki0112/awesome-deeplearning-study

Summary of DeepLearning (Korean and English are included)
https://github.com/taki0112/awesome-deeplearning-study

List: awesome-deeplearning-study

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Summary of DeepLearning (Korean and English are included)

Host: GitHub
URL: https://github.com/taki0112/awesome-deeplearning-study
Owner: taki0112
License: mit
Created: 2017-09-22T01:52:11.000Z (about 7 years ago)
Default Branch: master
Last Pushed: 2018-12-27T08:39:29.000Z (almost 6 years ago)
Last Synced: 2023-03-02T19:47:10.836Z (almost 2 years ago)
Language: Python
Homepage:
Size: 90.1 MB
Stars: 93
Watchers: 12
Forks: 25
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

ultimate-awesome - awesome-deeplearning-study - Summary of DeepLearning (Korean and English are included). (Other Lists / Monkey C Lists)

README

        # DeepLearning-Summary

*Continually updating...*

## PR12 (Korean)

* [PR12-Season1](https://www.youtube.com/playlist?list=PLWKf9beHi3Tg50UoyTe6rIm20sVQOH1br)

* [PR12-Season2](https://www.youtube.com/playlist?list=PLWKf9beHi3TgstcIn8K6dI_85_ppAxzB8)

* [Sung-Kim-PR12](https://www.youtube.com/watch?v=auKdde7Anr8&list=PLlMkM4tgfjnJhhd4wn5aj8fVTYJwIpWkS)

* [PR12-archive](https://jamiekang.github.io/archives/)

## Pre-processing

* [python-library](http://www.lfd.uci.edu/~gohlke/pythonlibs/)

* [Image-augmentation](https://github.com/aleju/imgaug)

## CheatSheet

* [shervine](https://stanford.edu/~shervine/) **(recommend)**

## Mathematics

#### English

* [Probability-and-Statistics-Cookbook](http://statistics.zone/)

* [bayes-nn](https://github.com/sjchoi86/bayes-nn)

* [Mathematics-for-Machine-Learning](https://mml-book.github.io/)

* [Mathematics-for-Machine-Learning_2](https://www.doc.ic.ac.uk/~mpd37/teaching/2017/496/notes.pdf?fbclid=IwAR0qShb6-PpjAd9RvLuAmmL2dWTeg1OY4oDGutSxOJcWXXiL3MHdnP3PsCs)

#### Korean

* [수포자를 위한 머신러닝](https://github.com/taki0112/Awesome-DeepLearning-Study/blob/master/file/%EC%88%98%ED%8F%AC%EC%9E%90%EB%A5%BC%20%EC%9C%84%ED%95%9C%20%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D.pdf)

* [Mathematics-Cookbook](https://github.com/taki0112/Awesome-DeepLearning-Study/blob/master/file/Mathematics-Cookbook.pdf)

* [Dimension-Reduction](https://github.com/taki0112/DeepLearning-Summary/raw/master/file/Dimensionality_Reduction.pdf)

* [Pattern-Recognition](http://norman3.github.io/prml/)

* [공돌이의 수학정리 노트](https://wikidocs.net/book/563)

* [Convex-Optimization](https://wikidocs.net/18720)

## Theory

#### English

* [Machine-Learning-glossary](https://developers.google.com/machine-learning/glossary/)

* [Backpropagation-In-Convolutional-Neural-Networks](http://www.jefkine.com/general/2016/09/05/backpropagation-in-convolutional-neural-networks/)

* [1x1-Convolution](http://iamaaditya.github.io/2016/03/one-by-one-convolution/)

* [SNU-TF](https://drive.google.com/drive/folders/0B8z5oUpB2DysbFNEOWxfVDh5VW8)

* [Object-Detection](http://www.telesens.co/2018/03/11/object-detection-and-classification-using-r-cnns/)

#### Korean

* [K-MOOC_lecture](http://www.kmooc.kr/)

* [python_lecture](https://www.youtube.com/playlist?list=PLBHVuYlKEkUJvRVv9_je9j3BpHwGHSZHz&disable_polymer=true)

* [딥러닝 이론에서 실습까지](https://drive.google.com/file/d/0B-qyuGELhRZ6dGhFeUNVaFNPbms/view)

* [Machine-Learning](http://sanghyukchun.github.io/)

* [Korea-Univ-Jaegul-Choo](https://www.youtube.com/channel/UCsEQc1-iFbu_yHvMd1vqwFQ/playlists)

## TechBlog

#### English

* [GAN-wiseodd](https://wiseodd.github.io/techblog/)

* [Peter's notes](http://peterroelants.github.io/)

* [Colah](http://colah.github.io/)

* [Distill](https://distill.pub/)

* [pyimagesearch](https://www.pyimagesearch.com/author/adrian/) **(recommend)**

* [Neurohive](https://neurohive.io/en/#pll_switcher) **(recommend)**

* [Papers with code](https://paperswithcode.com/?ref=semscholar) **(recommend)**

#### Korean

* [Carpedm20](http://carpedm20.github.io/)

* [Lunit](https://blog.lunit.io/)

* [LAON-PEOPLE](http://blog.naver.com/laonple/220469250655)

* [Deepest](http://deepestdocs.readthedocs.io/en/latest/)

* [Sung-Kim_Summary](http://pythonkim.tistory.com/notice/25)

* [조대협](http://bcho.tistory.com/category/%EB%B9%85%EB%8D%B0%EC%9D%B4%ED%83%80/%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D)

* [Chris-송호연](https://brunch.co.kr/@chris-song#articles)

* [ratsgo-blog](https://ratsgo.github.io/blog/categories/)

* [Keunwoo-choi](http://keunwoochoi.blogspot.kr/)

## DeepLearning Lecture

#### English

* [Coursera, Machine Learning - Andrew Ng](https://www.coursera.org/)

* [DLSS, RLSS](http://videolectures.net/deeplearning2017_montreal/)

* [fast.ai](http://course.fast.ai/?utm_campaign=Revue+newsletter&utm_medium=Newsletter&utm_source=revue)

* [Siraj Raval](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A)

* [cs231n](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv)

#### Korean

* [Sung-Kim](https://www.youtube.com/channel/UCML9R2ol-l0Ab9OXoNnr7Lw)

* [최신논문으로 시작하는 딥러닝](http://www.edwith.org/deeplearningchoi/)

* [cs231n](https://www.youtube.com/playlist?list=PL1Kb3QTCLIVtyOuMgyVgT-OeW0PYXl3j5)

* [Terry-deeplearning-talk](https://www.youtube.com/watch?v=D4zqigCb8co&list=PL0oFI08O71gKEXITQ7OG2SCCXkrtid7Fq)

* [Reienforcement-slide](https://www.slideshare.net/WoongwonLee/ss-78783597?from_m_app=android)

* [NaverD2](https://www.youtube.com/watch?v=soZXAH3leeQ&list=PLsFtzQAC8dDetav3jSCKB_MXwvUn7yfJS)

* [Enjoy-DL](https://www.facebook.com/notes/enjoydl/deep-leaning-video-links/1227281394020895/)

## MachineLearning Lecture

#### English

* [Awesome-Machine-Learning-Projects](https://ml-showcase.com/)

#### Korean

* [KAIST_lecture](https://www.youtube.com/watch?v=4w1lidx6mV4&list=PLbhbGI_ppZIRPeAjprW9u9A46IJlGFdLn)

* [Summary_slide](https://www.slideshare.net/SGoodKim/machine-learning-bysogood-80076209)

## DeepLearning Tutorial

#### English

* [sjchoi_tutorial](https://github.com/sjchoi86/dl_tutorials_10weeks)

* [DeepLearningZeroToAll](https://github.com/hunkim/DeepLearningZeroToAll)

#### Korean

* [Yongho Ha_slide](https://www.slideshare.net/yongho/ss-79607172)

* [Namhyuk-Ahn](https://github.com/nmhkahn/deep_learning_tutorial)

* [Chanwoo-Lee](http://nbviewer.jupyter.org/format/slides/gist/leechanwoo/)

## Tensorflow Tutorial

#### Korean

* [TensorFlowKR-2017-talk-bestpractice_slide](https://github.com/wookayin)

* [Golbin_tutorial](https://github.com/golbin/TensorFlow-Tutorials)

### English

* [Keras + Tensorflow for image](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)

* [Stanford_Tensorflow_tutorial](http://web.stanford.edu/class/cs20si/)

## Pytorch Tutorial

### English

* [yunjey](https://github.com/yunjey/pytorch-tutorial)

* [PyTorchZeroToAll](https://github.com/hunkim/PyTorchZeroToAll)

### Korean

* [Choi-Gunho](https://github.com/GunhoChoi/PyTorch-FastCampus)

## GAN, VAE

#### English

* [illinois_slide](http://slazebni.cs.illinois.edu/spring17/lec11_gan.pdf)

* [Delving-deep-into-GANs](https://github.com/GKalliatakis/Delving-deep-into-GANs)

* [Wasserstein GAN in Keras](https://myurasov.github.io/2017/09/24/wasserstein-gan-keras.html?r)

* [tf-gan-comparison](https://github.com/khanrc/tf.gans-comparison)

* [tf-gan-colletion](https://github.com/hwalsuklee/tensorflow-generative-model-collections)

* [how-to-train-gan](https://github.com/taki0112/Awesome-DeepLearning-Study/raw/master/file/How_To_Train_a_GAN.pdf)

#### Korean

* [Hwalsuk Lee_slide](https://mega.nz/#!tBo3zAKR!yE6tZ0g-GyUyizDf7uglDk2_ahP-zj5trVZSLW3GAjw)

* [Jaejun Yoo_blog](http://jaejunyoo.blogspot.com/search/label/GAN)

* [Style-Transfer](https://github.com/taki0112/DeepLearning-Summary/raw/master/file/Style%20Transfer.pdf)

## NLP

#### English

* [stanford-NLP](https://www.youtube.com/watch?v=OQQ-W_63UgQ&list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6)

* [oxford-NLP](https://github.com/oxford-cs-deepnlp-2017/lectures), [video](https://www.youtube.com/watch?v=RP3tZFcC2e8&list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm)

* [NLP-DL-Lecture-Note](https://github.com/nyu-dl/NLP_DL_Lecture_Note)

* [NeuralDialogPaper](https://github.com/snakeztc/NeuralDialogPapers)

* [Word2Vec](http://adventuresinmachinelearning.com/word2vec-tutorial-tensorflow/)

#### Korean

* [Word2Vec](https://dreamgonfly.github.io/machine/learning,/natural/language/processing/2017/08/16/word2vec_explained.html)

* [RNN](http://jaejunyoo.blogspot.com/2017/06/anyone-can-learn-to-code-LSTM-RNN-Python.html)

* [NaverD2_lecture](https://www.youtube.com/watch?v=r0veZ_WV0sA&list=PLsFtzQAC8dDdIqSY3o5XF_IBIgSLcyzTd)

## etc

#### English

* [visualize-neural-net](https://github.com/Cloud-CV/Fabrik)

* [visualize-convoulution](https://github.com/vdumoulin/conv_arithmetic)

* [visualize-GAN](https://reiinakano.github.io/gan-playground/)

* [visualize-learning_rate](http://www.benfrederickson.com/numerical-optimization/)

#### Korean

* [Medical-AI_slide](https://www.slideshare.net/hyunseokmin/applying-deep-learning-to-medical-data?from_m_app=android)