안녕하세요.
4주차(6월03일_토) 스터디 진행 사항과 참여 인원이 하기와 같음을 알려 드립니다.
4주차 스터디/동영상 내용과 관련하여 토의 사항이 있을 경우 댓글로 남겨 주시면 감사하겠습니다.
- 학습진도:
-- 동영상 :
--- 복습 : ML lec 5-1 Logistic Classification의 가설 함수 정의 ,
--- 복습 : ML lec 5-2 Logistic Regression의 cost 함수 설명 ,
--- ML lab 05: TensorFlow로 Logistic Classification의 구현하기 (new) ,
--- ML lec 6-1 - Softmax Regression: 기본 개념 소개 ,
--- ML lec 6-2: Softmax classifier 의 cost함수 ,
--- ML lab 06-1: TensorFlow로 Softmax Classification의 구현하기
-- 논문 Q&A :
--- Deep Neural Networks for YouTube Recommendations
- 참여인원: 총 9 명
- 참고:
- Pattern Recognition and Machine Learning
- Machine Learning | Coursera - Andrew Ng
-- Stanford Machine Learning - 06: Logistic Regression
-- CS 229 Machine Learning Course Materials
- CS 229 Machine Learning | Stanford - Andrew Ng - Lecture 3
- Neural Networks for Machine Learning | Coursera - Prof. Geoffrey Hinton on Coursera in 2013
-- Neural Networks for Machine Learning | YouTube - Prof. Geoffrey Hinton on Coursera in 2012
--- Lecture 4.3 — The softmax output function [Neural Networks for Machine Learning]
-- CSC321 Winter 2014 - Lecture notes
-- (기계 학습, Machine Learning) Week 4 Logistic Regression
--- Lecture 1 Decision Boundary
--- Lecture 2 Introduction to Logistic Regression
- BerkeleyX: CS120x Distributed Machine Learning with Apache Spark
-- Linear Classification and Logistic Regression
-- Logistic Regression: Probabilistic Interpretation
- Machine Learning in Python - Logistic regression in machine learning
- Machine Learning 강의노트 - 03. Logistic Regression
- CS224D Lecture 7 - Introduction to TensorFlow (19th Apr 2016)
- CS224n Lecture 7 - Introduction to TensorFlow (31 January, 2017)
- CS 229 Machine Learning Course Materials | Standford
-- Linear Algebra Review and Reference
-- Binary classification with +/-1 labels
-- Supervised Learning, Discriminative Algorithms
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