혁펜하임 | AI & 딥러닝 강의
혁펜하임 | AI & 딥러닝 강의
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[Easy! 딥러닝] 8-6강. 정규화(Regularization)와 MAP(Maximum A Posteriori) [[ 실험 영상 포함! ]]
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---------------------------------------------
0:00 - Regularization 개념
2:47 - 왜 파라미터 크기를 줄이려고 할까?
6:27 - 직관적으로 이해해보자
8:58 - MAP로 해석
16:29 - 실험
19:15 - 오버피팅 방지 방법들 모음
20:00 - 마무리, AI DEEP DIVE 소개
มุมมอง: 2 393

วีดีโอ

[Easy! 딥러닝] 8-5강. 당신이 몰랐던 Dropout에 대한 진실 [[ 비교 실험 포함! ]]
มุมมอง 1.8K2 หลายเดือนก่อน
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[Easy! 딥러닝] 8-4강. 과적합(overfitting)과 데이터 증강(data augmentation)
มุมมอง 1.2K2 หลายเดือนก่อน
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[Easy! 딥러닝] 8-3강. Loss Landscape이 꼬불꼬불 해진다?! [[ 실험 영상 포함! ]]
มุมมอง 1.4K2 หลายเดือนก่อน
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[Easy! 딥러닝] 8-2강. 이 영상 하나로 끝내세요! | 배치 정규화 & 레이어 정규화 (Batch Normalization & Layer Normalization)
มุมมอง 2K3 หลายเดือนก่อน
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[Easy! 딥러닝] 8-1강. Vanishing Gradient (기울기 소실) 와 ReLU [[ 실험 영상 포함! ]]
มุมมอง 2.3K3 หลายเดือนก่อน
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[Easy! 딥러닝] 7강. 영상 안 보고 풀면 천재! | Universal Approximation Theorem (보편 근사 정리)
มุมมอง 2.2K3 หลายเดือนก่อน
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[Easy! 딥러닝] 6-5강. 소프트맥스 회귀(Softmax regression)의 모든 것 | 다중 분류 (multiclass classification)
มุมมอง 1.6K3 หลายเดือนก่อน
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[Easy! 딥러닝] 6-4강. 이거 많이 어렵습니다.. 상위 1%만 알고 있는 딥러닝의 뿌리 이론! MLE (Maximum Likelihood Estimation)
มุมมอง 2.3K3 หลายเดือนก่อน
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[Easy! 딥러닝] 6-3강. MSE vs BCE 비교 분석
มุมมอง 2.7K4 หลายเดือนก่อน
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[Easy! 딥러닝] 6-2강. 로지스틱 회귀(Logistic regression)의 모든 것 | 이진 분류 (binary classification)
มุมมอง 2.7K4 หลายเดือนก่อน
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[Easy! 딥러닝] 6-1강. 왜 이진 분류에서 sigmoid를 사용할까?
มุมมอง 2.4K4 หลายเดือนก่อน
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[Easy! 딥러닝] 5-2강. 역전파(Backpropagation) 세상에서 가장 쉬운 설명!
มุมมอง 3K4 หลายเดือนก่อน
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[Easy! 딥러닝] 5-1강. 인공 신경망을 바라보는 통찰력이 생긴다..! | linear activation이 중간에 들어가면??
มุมมอง 3.4K5 หลายเดือนก่อน
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[Easy! 딥러닝] 4-2강. K-fold Cross Validation (교차 검증) 진짜 쉽게 설명해 드려요
มุมมอง 2.4K5 หลายเดือนก่อน
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[Easy! 딥러닝] 4-1강. Validation 데이터가 꼭 필요한 이유 | 테스트 데이터와 차이점은??
มุมมอง 2.7K6 หลายเดือนก่อน
[Easy! 딥러닝] 4-1강. Validation 데이터가 꼭 필요한 이유 | 테스트 데이터와 차이점은??
[Easy! 딥러닝] 3-6강. Adam (Adaptive Moment Estimation) 완벽 정리!
มุมมอง 3.3K6 หลายเดือนก่อน
[Easy! 딥러닝] 3-6강. Adam (Adaptive Moment Estimation) 완벽 정리!
[Easy! 딥러닝] 3-5강. RMSProp 수식 없이 설명하기
มุมมอง 2.2K6 หลายเดือนก่อน
[Easy! 딥러닝] 3-5강. RMSProp 수식 없이 설명하기
[Easy! 딥러닝] 3-4강. momentum 쉬운 설명 | 치타만 기억하세요!
มุมมอง 2.5K6 หลายเดือนก่อน
[Easy! 딥러닝] 3-4강. momentum 쉬운 설명 | 치타만 기억하세요!
(( 마감 임박 !! )) 2024 AWS 써밋 서울 | 코엑스에서 만나요~!!
มุมมอง 1.3K6 หลายเดือนก่อน
(( 마감 임박 !! )) 2024 AWS 써밋 서울 | 코엑스에서 만나요~!!
[Easy! 딥러닝] 3-3강. mini-batch GD & 배치 크기를 무작정 키우면 안 되는 이유
มุมมอง 4.5K7 หลายเดือนก่อน
[Easy! 딥러닝] 3-3강. mini-batch GD & 배치 크기를 무작정 키우면 안 되는 이유
[Easy! 딥러닝] 3-2강. 확률적 경사 하강법 (SGD: Stochastic Gradient Descent) 주머니 예시로 쉽게 설명해 드려요
มุมมอง 2.7K7 หลายเดือนก่อน
[Easy! 딥러닝] 3-2강. 확률적 경사 하강법 (SGD: Stochastic Gradient Descent) 주머니 예시로 쉽게 설명해 드려요
[Easy! 딥러닝] 3-1강. Gradient Descent 의 두 가지 치명적 단점
มุมมอง 2.6K7 หลายเดือนก่อน
[Easy! 딥러닝] 3-1강. Gradient Descent 의 두 가지 치명적 단점
현직 웹 백엔드 개발자 2년 차의 현실적인 고민
มุมมอง 4.8K7 หลายเดือนก่อน
현직 웹 백엔드 개발자 2년 차의 현실적인 고민
[Easy! 딥러닝] 2-5강. 웨이트 초기화 기법들 빠르게 정리해 드려요
มุมมอง 3K7 หลายเดือนก่อน
[Easy! 딥러닝] 2-5강. 웨이트 초기화 기법들 빠르게 정리해 드려요
[Easy! 딥러닝] 2-4강. 경사 하강법 (Gradient Descent) | step by step 으로 차근차근 알아보기
มุมมอง 5K8 หลายเดือนก่อน
[Easy! 딥러닝] 2-4강. 경사 하강법 (Gradient Descent) | step by step 으로 차근차근 알아보기
[Easy! 딥러닝] 2-3강. 이게 이해되면 딥러닝도 이해됨! | 선형 회귀 (Linear Regression)
มุมมอง 4K8 หลายเดือนก่อน
[Easy! 딥러닝] 2-3강. 이게 이해되면 딥러닝도 이해됨! | 선형 회귀 (Linear Regression)
[Easy! 딥러닝] 2-2강. 인공 신경망은 함수다!
มุมมอง 3.2K9 หลายเดือนก่อน
[Easy! 딥러닝] 2-2강. 인공 신경망은 함수다!
와.. 이 좋은 걸 여태 몰랐네.. AWS "전액 무료" AI 활용 온라인 강의!!
มุมมอง 3.1K9 หลายเดือนก่อน
와.. 이 좋은 걸 여태 몰랐네.. AWS "전액 무료" AI 활용 온라인 강의!!
[Easy! 딥러닝] 2-1강. 인공 신경망 비유로 쉽게 이해하기!
มุมมอง 4.1K9 หลายเดือนก่อน
[Easy! 딥러닝] 2-1강. 인공 신경망 비유로 쉽게 이해하기!

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  • @강동건-n1i
    @강동건-n1i 12 ชั่วโมงที่ผ่านมา

    TD학습에서 N번 샘플을 뽑는다고 하셨는데, 상태t 에서 t+1의 상태와 액션을 policy를 이용해서 뽑는것을 N번 반복해서 그렇게 구해진 Q값과 리워드 값들을 평균 낸것 이라고 이해해도 될까요?

    • @hyukppen
      @hyukppen 12 ชั่วโมงที่ผ่านมา

      타임스탬프 달아주시겠어요?

    • @강동건-n1i
      @강동건-n1i 11 ชั่วโมงที่ผ่านมา

      4분 33초 정도 인것 같습니다

    • @hyukppen
      @hyukppen 10 ชั่วโมงที่ผ่านมา

      아아 그거는 큰 수의 법칙 이용한 것입니다 ㅎㅎ

  • @unmewhy
    @unmewhy 15 ชั่วโมงที่ผ่านมา

    웃김. . 천천히 공부하라고 하는데. . 무슨 딥러닝부터임?

  • @SHLee-vp5ey
    @SHLee-vp5ey 19 ชั่วโมงที่ผ่านมา

    마지막 문제 힌트: X_w0(w)를 새로운 주기함수로 생각해서 푸리에 급수 전개

  • @chotnt
    @chotnt 20 ชั่วโมงที่ผ่านมา

    앤드류응 혁펜하임 렛츠고

  • @ringo8530
    @ringo8530 วันที่ผ่านมา

    왜 그라디언트가 평행이어야 되는지 이해가 쑥쑥 됩니다. 감사합니다 혁펜센세

  • @JohnLee-vt7pj
    @JohnLee-vt7pj 2 วันที่ผ่านมา

    카이스트 박사가 이런 얘기를 한다는거 자체가.... 그닥이네요.... 논문 읽는 법을 알려줘야지 ㅋㅋㅋㅋ

  • @Ppp5tt
    @Ppp5tt 2 วันที่ผ่านมา

    근데 공대면은 2학년때부턴 종합대도 학점챙기고, 전공공부하느라 정신없다고 들었는데, 그 '서울라이프'라는게 그렇게 인생에 가장 중요한 선택을 하는데 있어서 큰 요인인가요? 카이다닌다고 해서 서울 못올라가는것도 아니고, 더군다나 공대라면 학점안나오면 취업도 불리할텐데. 주변에서 서울라이프서울라이프하는거 때문에 좀 '세뇌(?)'되는 면이 있는데, 그렇게까지 목 멜 이유가 되는지는 모르겠네요

  • @ro-by8nb
    @ro-by8nb 3 วันที่ผ่านมา

    선대 드르륵 드르륵 이후로 액웨액나가 또 머리에 박히네

  • @무멘-p5h
    @무멘-p5h 3 วันที่ผ่านมา

    좋은 영상 감사합니다

  • @ro-by8nb
    @ro-by8nb 3 วันที่ผ่านมา

    선대부터 듣고있는데 설명력 감탄…🎉🎉🎉🎉

  • @RealWarm
    @RealWarm 3 วันที่ผ่านมา

    멋진말 너무 감사합니다

  • @925a
    @925a 4 วันที่ผ่านมา

    법학 논문은 애가 말을 지어내서 안됨.. ㅠㅠ 법논리는 불가능한듯

  • @edk.2302
    @edk.2302 4 วันที่ผ่านมา

    영상 내용과는 살짝 어긋나는것 같지만 질문 하나만 하겠습니다! 다른게 아니라 선형 대수에서 vector 혹은 matrix 간에 linear operation을 할 때 inner dimension이 match 해야되잖아요? 손으로 선형대수 문제 풀때는 그게 쉬운데 이걸 코드로 볼때는 좀 많이 헷갈리더라고요. 혁펜하임님 만의 팁 같은게 있을까요? 감사합니다!

    • @hyukppen
      @hyukppen 4 วันที่ผ่านมา

      match가 안되면 에러가 뜨겠죠? 어떤 팁을 원하시는 건지 잘 이해가 안 갔습니다 😅

  • @soyeonilgi
    @soyeonilgi 5 วันที่ผ่านมา

    진짜 보다가 감동 받았어요...

  • @Srcb-q7s
    @Srcb-q7s 6 วันที่ผ่านมา

    GD가 느리다는게 이해가 안갑니다. GD는 모든 데이터를 고려해서 평균을 내 한번에 업데이트하고, SGD는 개별 데이터를 고려해서 여러번 이동한다 인 것 같은데, 결국 모든 데이터를 고려하는건 똑같은 것 아닌가요? 계산 속도 차원에서 느리다는 것이 이해가 안갑니다..

    • @hyukppen
      @hyukppen 6 วันที่ผ่านมา

      5:53의 설명을 봐주시면 이해가 될 것 같습니다 ㅎㅎ 물론 병렬연산을 배제하고 설명드린 것입니다

  • @YouTube김늑태
    @YouTube김늑태 8 วันที่ผ่านมา

    하앍 뇌수 싸버렷

  • @아아스베
    @아아스베 8 วันที่ผ่านมา

    시험까지 8시간남짓... 드디어 뚫었다!!!! 감사함동

  • @이영주-p3m
    @이영주-p3m 8 วันที่ผ่านมา

    너무 멋지네요....군더더기 하나 없는 강의....최고 입니다....

  • @SHLee-vp5ey
    @SHLee-vp5ey 8 วันที่ผ่านมา

    "a_k는 직교기저 e^kw0t에 대한 좌표이다" 라고 이해하는게 맞을까요? 선대 공부한지 너무 오래되어서 가물가물하네요.

    • @hyukppen
      @hyukppen 8 วันที่ผ่านมา

      @@SHLee-vp5ey a_k만큼 해당 basis가 존재한다고 보시면 될 것 같습니다. 3x^2+x-1에서 3을 뭐라고 말하면 좋을까? 로 바꿔 생각해 보세요~!

  • @cacbon-dioxit
    @cacbon-dioxit 9 วันที่ผ่านมา

    0:20 notation이나 "스칼라를 벡터로 미분"한다는 말에서 뇌정지가 오시는 분을 위해... 만약 함수 f를 미분함을 나타내려고 df/dx라고 표기했는데, 분모에 볼드체로 적힌 벡터가 왔다는 건 애초부터 f가 받는 입력이 벡터였다는 뜻입니다. 그래서 고등학교 때 일변수함수에서 했던 것처럼 입력에 대한 출력의 변화량을 구하고자 한다는 것에 지나지 않습니다. 다변수 벡터 함수도 마찬가지겠죠. 저건 출력을 입력으로 미분한다는 것을 생각하고 이야기를 한다고 보면 받아들이기 편합니다.

  • @qwerasd1
    @qwerasd1 10 วันที่ผ่านมา

  • @qwerasd1
    @qwerasd1 10 วันที่ผ่านมา

    4:53 혁펜하임 폭주..

  • @NotoriousThug901
    @NotoriousThug901 11 วันที่ผ่านมา

    용어때문에 번역 해봤자 의미가 없는… 어차피 용어를 다 영어로 쓰는데 번역하면 오히려 읽기 나쁘던데.

  • @entry_edobe
    @entry_edobe 11 วันที่ผ่านมา

    으아 31개의 영상이 제 신경망을 벨리데이션 없이 트레이닝과 테스트로만 해봤는데 테스트 데이터를 노드로 하니 로스가 너무 커졌지만 점점 y좌표를 조정하고 데이터 증강으로 본 영상을 여러개의 데이터로 만들었습니다 흠... 너무 억지인가

  • @nova0302
    @nova0302 12 วันที่ผ่านมา

    역으로 global minimum 으로부터 탈출의 기회가 되기도...😢

  • @최성민-g4o
    @최성민-g4o 12 วันที่ผ่านมา

    뻥이 아니라 학교 수업보다 이해 100배 잘됩니다...

  • @뚜딘-s1t
    @뚜딘-s1t 13 วันที่ผ่านมา

    짱짱 🙌🏾

  • @뚜딘-s1t
    @뚜딘-s1t 13 วันที่ผ่านมา

    스펀지 ㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋ 인공신경망은 네모다~~~~~할때 목소리 너무 좋아요 ㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋ 짱웃김 집중잘됩니당 💜

  • @뚜딘-s1t
    @뚜딘-s1t 13 วันที่ผ่านมา

    오늘도 감사합니다. 얼른 따라갈게요!

  • @user-y5s8m
    @user-y5s8m 13 วันที่ผ่านมา

    0:01

  • @멍멍튜브구르미
    @멍멍튜브구르미 13 วันที่ผ่านมา

    좋은강의 감사감자합니다♡

  • @chotnt
    @chotnt 13 วันที่ผ่านมา

    멍하다가 베개 설명 떠오르면서 이해가 되었네요 ㅋㅋ

  • @crymtonym
    @crymtonym 14 วันที่ผ่านมา

    엑셀 수식 물어보기 개꿀 ㅋ

  • @뚜딘-s1t
    @뚜딘-s1t 14 วันที่ผ่านมา

    너무 좋은 강의 감사합니다! 🫰

  • @chotnt
    @chotnt 14 วันที่ผ่านมา

    공부하다 졸렸는데 인트로보고 잠 깼습니다. 감사해요

  • @이영주-p3m
    @이영주-p3m 14 วันที่ผ่านมา

    엑기스 알짜 강의입니다... 의도적인건지 모르겠으나 구독자 들을 위한 배려가 느껴집니다...