안녕하세요.
7주차(6월24일_토) 스터디 진행 사항과 참여 인원이 하기와 같음을 알려 드립니다.
7주차 스터디/동영상 내용과 관련하여 토의 사항이 있을 경우 댓글로 남겨 주시면 감사하겠습니다.
- 학습진도:
-- 동영상 :
--- lec9-1: XOR 문제 딥러닝으로 풀기 ,
--- lec9-x: 특별편: 10분안에 미분 정리하기 (lec9-2 이전에 보세요) ,
--- lec9-2: 딥넷트웍 학습 시키기 (backpropagation) ,
--- ML lab 09-1: Neural Net for XOR
-- 동영상(복습) :
--- ML lec 5-1: Logistic Classification의 가설 함수 정의
- 참여인원: 총 6 명
- 예제코드:
- lab-03-1-minimizing_cost_show_graph
- 참고:
- Machine Learning | Coursera - Andrew Ng
-- 3.1.1 Logistic Regression - Classification | YouTube
-- Stanford Machine Learning - 06: Logistic Regression
-- CS 229 Machine Learning Course Materials
- CS 229 Machine Learning | Stanford - Andrew Ng - Lecture 3
- First Contact with TensorFlow(원서_번역내용) | 텐서플로우 블로그 (Tensor ≈ Blog)
- TensorFlow Tutorial | YouTube
-- TensorFlow Tutorials | GitHub
- CS 20SI: Tensorflow for Deep Learning Research
-- CS 20SI: Tensorflow for Deep Learning Research(YouTube)
- CS224D Lecture 7 - Introduction to TensorFlow (19th Apr 2016)
- CS224n Lecture 7 - Introduction to TensorFlow (31 January, 2017)
- Pattern Recognition and Machine Learning
- 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
- 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
- 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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