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:

-- 인공지능과 기계학습_KMOOC

--- 1.4-(Naive Bayes) ,

--- 2.1-VC Dimension (Basic Concepts Margin and VC Dimension)

 ​- 도서 리뷰:

-- 밑바닥부터 시작하는 딥러닝

--- 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 

-- Lecture Notes | Coursera

-- 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

-- Schedule and Syllabus

- 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)

-- DeepLearningBook 

-- 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