안녕하세요.
13주차(8월 05일_토) 스터디 내용과 참여 인원이 하기와 같았음을 알려 드립니다.
13주차 스터디/동영상 내용과 관련하여 토의 사항이 있을 경우 댓글로 남겨 주시면 감사하겠습니다.
- 학습진도:
- 동영상:
--- 1.4-(Naive Bayes) ,
--- 2.1-VC Dimension (Basic Concepts Margin and VC Dimension)
- 도서 리뷰:
--- 4장: 신경망 학습
- 참여인원: 총 6 명
- 관련 포스트 or 논문:
- Naive Bayes Classification
-- Naive Bayes Theorem(post)
-- Naive Bayes Classification explained with Python code(post)
-- How To Implement Naive Bayes From Scratch in Python(post)
-- 6 Easy Steps to Learn Naive Bayes Algorithm (with code in Python)(post)
-- A Programmer's Guide to Data Mining
--- Naïve Bayes and Probability Density Functions(post)
--- Python code for the free book A Programmer's Guide to Data Mining | GitHub
- 참고 웹사이트:
- Machine Learning | Coursera - Andrew Ng
-- CS 229 Machine Learning Course Materials
- CS231n: Convolutional Neural Networks for Visual Recognition(Winter 2016)
-- CS231n Winter 2016 | YouTube
--- CS231n Winter 2016: Lecture 5: Neural Networks Part 2
--- CS231n Winter 2016: Lecture 6: Neural Networks Part 3 / Intro to ConvNets
--- CS231n Winter 2016: Lecture 7: Convolutional Neural Networks
--- CS231n Winter 2016: Lecture 10: Recurrent Neural Networks, Image Captioning, LSTM
-- Schedule and Syllabus(Winter 2016)
- CS224n: Natural Language Processing with Deep Learning
-- Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017) | YouTube
--- Lecture 8: Recurrent Neural Networks and Language Models
--- Lecture 13: Convolutional Neural Networks
- Deep Learning Book Review | YouTube
-- Online Discussion: Deep Learning Book Ch 3
-- Online Discussion: Deep Learning Book Ch 9 (Part 1)
-- Online Discussion: Deep Learning Book Ch 9 (Part 2)
-- Online Discussion: Deep Learning Book Ch 10 (Part 1)
-- Online Discussion: Deep Learning Book Ch 10 (Part 2)
-- Deep Learning_An MIT Press book - Ian Goodfellow and Yoshua Bengio and Aaron Courville
--- Chapter 3: Probability and Information Theory
--- Chapter 9 : Convolutional Networks
--- Chapter 10 : Sequence Modeling: Recurrent and Recursive Nets
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