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CourseraMachineLearning으로
기계학습배우기:week4
1
개요
지난시간에이어CorseraMachineLearning으로기계학습배우기:week4
정리를진행한다.
해당포스팅은연재글로써지난연재는아래의링크를참고한다.
CorseraMachineLearning으로기계학습배우기:week1
CorseraMachineLearning으로기계학습배우기:week2
CorseraMachineLearning으로기계학습배우기:week3
2
글을읽기에앞서…
본글은필자가코세라기계학습을공부를하는과정에서개념을확고히정
리하기위하는데목적이있다.(필자가나중에내용을다시찾아보기위한
목적이있다.)
코세라강의week개수에맞추어포스팅을진행할예정이다.
코세라의슬라이드에한글주석을단것이핵심으로내용에서글을읽을필
요없이슬라이드그림만으로최대한이해가되게끔하는데목적이있다.
수학은한국의고등수학을베이스로한다.수학적개념이나올때가급적
고등학교수학을베이스로내용을정리한다.
정리내용의목차구성을코세라강의와동일하게맞추고또한제목을원문
으로둔다.(원본강의내용과정리내용을서로서로찾아보기쉽게하기위
함이다.)
3
Motivations
Non‑linearHypothesis
4
Non‑linearHypothesis(1/5)
5
Non‑linearHypothesis(2/5)
6
Non‑linearHypothesis(3/5)
7
Non‑linearHypothesis(4/5)
8
Non‑linearHypothesis(5/5)
9
NeuronsandtheBrain
10
NeuronsandtheBrain(1/4)
11
NeuronsandtheBrain(2/4)
가설은각각의뇌세포는하나의학습유닛들이다는것이다.
12
NeuronsandtheBrain(3/4)
13
NeuronsandtheBrain(4/4)
14
NeuralNetworks
ModelRepresentationI
15
ModelRepresentationI(1/4)
16
ModelRepresentationI(2/4)
17
ModelRepresentationI(3/4)
18
ModelRepresentationI(4/4)
19
ModelRepresentationII
20
ModelRepresentationII(1/5)
21
ModelRepresentationII(2/5)
22
ModelRepresentationII(2/5)
23
ModelRepresentationII(3/5)
24
ModelRepresentationII(4/5)
x1~x3로부터나온출력을새로운feature로도출한다는의미이다.
25
ModelRepresentationII(5/5)
딥러닝은학습을최소5번해서평균및표준편차를줄여야의미가있다.
26
Application
ExamplesandIntuitionsI
27
ExamplesandIntuitionsI(1/3)
28
ExamplesandIntuitionsI(2/3)
29
ExamplesandIntuitionsI(3/3)
30
ExamplesandIntuitionsII
31
ExamplesandIntuitionsII(1/4)
32
ExamplesandIntuitionsII(2/4)
33
ExamplesandIntuitionsII(3/4)
34
ExamplesandIntuitionsII(4/4)
35
MulticlassClassification
output뉴런을분류개수만큼가져가는것이결과가정확한경향이있다.
36
MulticlassClassification(1/2)
37
MulticlassClassification(2/2)
38

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