Machine Learning: Difference between revisions

Latest revision as of 00:16, 27 March 2026

예비 지식

확률과 통계
선형대수학

분류(Classification)

Naive Bayesian Classifier
Artificial Neural Network

강화학습(Reinforce Learning)

강의
- UCL Course on RL
- Youtube
책
- Reinforcement Learning: An Introduction
- Fundamental of Reinforcement Learning
Slide
- Deep RL Tutorial - David Silver
- 텐서플로우 설치도 했고 튜토리얼도 봤고 기초 예제도 짜봤다면 TensorFlow KR Meetup 2016
Articles
- Simple Reinforcement Learning with Tensorflow 한국어 번역
Resource
- OpenAI Gym : 실습 가능한 환경을 제공
Code
- tensorflow tutorial

링크들

Links

https://www.coursera.org/course/neuralnets

@@ Line 5: / Line 5: @@
 == 분류(Classification) ==
 * [http://wiki.zeropage.org/wiki.php/Naive%20Bayesian%20Classifier Naive Bayesian Classifier]
-* [[Artificial Neural Network]]
+* Artificial Neural Network
+== 강화학습(Reinforce Learning) ==
+* 강의
+** [http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html UCL Course on RL]
+** [https://www.youtube.com/watch?v=2pWv7GOvuf0 Youtube]
+* 책
+** [http://incompleteideas.net/sutton/book/bookdraft2017june19.pdf Reinforcement Learning: An Introduction]
+** [https://dnddnjs.gitbooks.io/rl/content Fundamental of Reinforcement Learning]
+* Slide
+** [http://icml.cc/2016/tutorials/deep_rl_tutorial.pdf Deep RL Tutorial - David Silver]
+** [https://www.slideshare.net/carpedm20/ss-63116251 텐서플로우 설치도 했고 튜토리얼도 봤고 기초 예제도 짜봤다면 TensorFlow KR Meetup 2016]
+* Articles
+** [http://ishuca.tistory.com/391 Simple Reinforcement Learning with Tensorflow 한국어 번역]
+* Resource
+** [https://gym.openai.com OpenAI Gym] : 실습 가능한 환경을 제공
+* Code
+** [https://github.com/golbin/TensorFlow-Tutorials/tree/master/10%20-%20DQN tensorflow tutorial]
 == 링크들 ==
 * http://wiki.zeropage.org/wiki.php/MachineLearning%EC%8A%A4%ED%84%B0%EB%94%94
 * http://www.reddit.com/r/MachineLearning/comments/20i0vi/meta_collection_of_links_for_beginners_faq
+* http://peekaboo-vision.blogspot.kr/2013/01/machine-learning-cheat-sheet-for-scikit.html
+* [https://github.com/NVIDIA/DIGITS Deep Learning GPU Training System]
+== Links ==
+* https://www.coursera.org/course/neuralnets