MachineLearning:Tutorials

Machine learning 강좌.

Lecture

1. Machine learning
2. Supervised Learning
3. Regression analysis
4. Linear regression
5. Hypothesis

Math

• 정규 분포 (Normal distribution)

No category

• CNN
• Convolution
• e
• Gradient descent

라온피플 - IT 업계와 AI : 네이버 블로그

• http://blog.naver.com/laonple/220463627091

Local Download

List of leatures

• 머신 러닝 (Machine Learning) - Class 01 : 개요
• 머신 러닝 (Machine Learning) - Class 02 : Supervised Learning(지도 학습)
• 머신 러닝 (Machine Learning) - Class 03 : Unsupervised Learning (자율 학습, 비지도 학습)
• 머신 러닝 (Machine Learning) - Class 04 : Reinforcement Learning(강화 학습)
• 머신 러닝 (Machine Learning) - Class 05 : Neural Network
• 머신 러닝 (Machine Learning) - Class 06 : History of Neural Network
• 머신 러닝 (Machine Learning) - Class 07 : Basic Theory for Neural Network
• 머신 러닝 (Machine Learning) - Class 08 : Backpropagation for Neural Network(역전파)
• 머신 러닝 (Machine Learning) - Class 09 : Backpropagation for Neural Network(역전파)
• 머신 러닝 (Machine Learning) - Class 10 : Overfitting 개요
• 머신 러닝 (Machine Learning) - Class 11 : Overfitting에 대한 해결책 – 1. Regularization
• 머신 러닝 (Machine Learning) - Class 12 : Overfitting에 대한 해결책 - 2. 지능적 훈련 데이터 생성
• 머신 러닝 (Machine Learning) - Class 13 : Overfitting에 대한 해결책 - 3. Dropout
• 머신 러닝 (Machine Learning) - Class 14 : Neural Network - 학습 속도 저하 현상의 원인
• 머신 러닝 (Machine Learning) - Class 15 : Neural Network - 학습 속도 저하 문제와 "Cross-Entropy Cost Function"
• 머신 러닝 (Machine Learning) - Class 16 : Neural Network의 Activation Function 'SoftMax'
• 머신 러닝 (Machine Learning) - Class 17 : Neural Network의 'Hyperparameters'
• 머신 러닝 (Machine Learning) - Class 18 : Neural Network의 'Hyperparameters Optimization'
• 머신 러닝 (Machine Learning) - Class 19 : Neural Network의 "CNN(Convolutional Neural Network)"
• 머신 러닝 (Machine Learning) - Class 20 : Neural Network - Why CNN(Convolutional Neural Network)?
• 머신 러닝 (Machine Learning) - Class 21 : Neural Network - CNN(Convolutional Neural Network)의 구조?
• 머신 러닝 (Machine Learning) - Class 22 : CNN(Convolutional Neural Network) – "Convolutional Layer" - Part 1
• 머신 러닝 (Machine Learning) - Class 23 : CNN(Convolutional Neural Network) – "Convolutional Layer" - Part 2
• 머신 러닝 (Machine Learning) - Class 24 : CNN(Convolutional Neural Network) – "Best CNN Architecture"
• 머신 러닝 (Machine Learning) - Class 25 : Best CNN(Convolutional Neural Network) Architecture – "LeNet5"
• 머신 러닝 (Machine Learning) - Class 26 : Best CNN(Convolutional Neural Network) Architecture - AlexNet(Part1)
• 머신 러닝 (Machine Learning) - Class 27 : Best CNN(Convolutional Neural Network) Architecture - AlexNet(Part2)
• 머신 러닝 (Machine Learning) - Class 28 : Best CNN(Convolutional Neural Network) Architecture - AlexNet(Part3)
• 머신 러닝 (Machine Learning) - Class 29 : Best CNN(Convolutional Neural Network) Architecture - ZFNet(Part1)
• 머신 러닝 (Machine Learning) - Class 30 : Best CNN(Convolutional Neural Network) Architecture - ZFNet(Part2)
• 머신 러닝 (Machine Learning) - Class 31 : Best CNN(Convolutional Neural Network) Architecture - ZFNet(Part3)
• 머신 러닝 (Machine Learning) - Class 32 : Best CNN(Convolutional Neural Network) Architecture - GoogLeNet (part1)
• 머신 러닝 (Machine Learning) - Class 33 : Best CNN(Convolutional Neural Network) Architecture - GoogLeNet (part2)
• 머신 러닝 (Machine Learning) - Class 34 : Best CNN(Convolutional Neural Network) Architecture - GoogLeNet (part3)
• 머신 러닝 (Machine Learning) - Class 35 : Best CNN(Convolutional Neural Network) Architecture - GoogLeNet (part4)
• 머신 러닝 (Machine Learning) - Class 36 : Best CNN(Convolutional Neural Network) Architecture - GoogLeNet (part5)
• 머신 러닝 (Machine Learning) - Class 37 : [Break time] 머신 러닝 소개 - Part 1
• 머신 러닝 (Machine Learning) - Class 38 : Best CNN(Convolutional Neural Network) Architecture - GoogLeNet (part6)
• 머신 러닝 (Machine Learning) - Class 39 : [Break time] 머신 러닝 소개 - Part 2
• 머신 러닝 (Machine Learning) - Class 40 : Best CNN(Convolutional Neural Network) Architecture - VGGNet(Part1)
• 머신 러닝 (Machine Learning) - Class 41 : Best CNN(Convolutional Neural Network) Architecture - VGGNet(Part2)
• 머신 러닝 (Machine Learning) - Class 42 : Best CNN(Convolutional Neural Network) Architecture - OverFeat
• 머신 러닝 (Machine Learning) - Class 43 : Best CNN(Convolutional Neural Network) Architecture - ResNet(part1)
• 머신 러닝 (Machine Learning) - Class 44 : Best CNN(Convolutional Neural Network) Architecture - ResNet(part2)
• 머신 러닝 (Machine Learning) - Class 45 : Best CNN(Convolutional Neural Network) Architecture - ResNet(part3)
• 머신 러닝 (Machine Learning) - Class 46 : Best CNN(Convolutional Neural Network) Architecture - ResNet/Fast R-CNN(part4)
• 머신 러닝 (Machine Learning) - Class 47 : Best CNN(Convolutional Neural Network) Architecture - ResNet/Faster R-CNN(part5)
• 머신 러닝 (Machine Learning) - Class 48 : Best CNN(Convolutional Neural Network) Architecture - ResNet (part6)
• 머신 러닝 (Machine Learning) - Class 49 : Best CNN(Convolutional Neural Network) Architecture - ResNet (part7)
• 머신 러닝 (Machine Learning) - Class 50 : Best CNN(Convolutional Neural Network) Architecture - ResNet/Inception-V4 (part8)
• 머신 러닝 (Machine Learning) - Class 51 : CNN의 핵심 요소 기술 – Batch Normalization(Part1)
• 머신 러닝 (Machine Learning) - Class 52 : CNN의 핵심 요소 기술 – Batch Normalization(Part2)
• 머신 러닝 (Machine Learning) - Class 53 : CNN의 핵심 요소 기술 – Dropout(Part1)
• 머신 러닝 (Machine Learning) - Class 54 : CNN의 핵심 요소 기술 – Dropout(Part2)
• 머신 러닝 (Machine Learning) - Class 55 : CNN의 핵심 요소 기술 – Dropout(Part3)
• 머신 러닝 (Machine Learning) - Class 56 : CNN의 핵심 요소 기술 – Stochastic Pooing
• 머신 러닝 (Machine Learning) - Class 57 : CNN의 핵심 요소 기술 – Maxout

See also

• Deep learning
• Machine learning
• Creating Moe Illustrations Using AI Tutorial

Favorite site

• [추천] Neural Networks and Deep Learning
  • [추천] CHAPTER 1: Using neural nets to recognize handwritten digits ¹
    • [추천] 번역: Neural Network를 이용한 손글씨 인지 ²
  • CHAPTER 6: Deep learning - Introducing convolutional networks
• CS231n: Convolutional Neural Networks for Visual Recognition
  • [추천] CS231n Convolutional Neural Networks for Visual Recognition (스탠포드에서 강의했던 CNN 수업)
  • [추천] CS231n Convolutional Neural Networks for Visual Recognition ³
  • Module1 Neural Networks: CS231n_-_Module_1_Neural_Networks_L1-L8.zip
  • Module2 Convolutional Neural Networks: CS231n_-_Module_2_Convolutional_Neural_Networks_L1-L3.zip
  • Github: Notes and assignments for Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition ⁴
• [추천] UFLDL Tutorial: Welcome to the Deep Learning Tutorial! ⁵
  • UFLDL Tutorial: Softmax Regression
  • UFLDL Tutorial: Convolutional Neural Network (CNN을 쉽게 정리했으며, 실제 내부적으로 알고리즘이 어떻게 돌아가는지 체득할 수 있도록 샘플코드 문제도 있다)
  • [추천]UFLDL Turotial: Convolutional Neural Network ⁶
  • UFLDL Tutorial (KOR)
• [추천] 머신러닝에서 딥러닝까지 (Machine Learning To Deep Learning) ⁷ (머신 러닝이나 뉴럴넷을 인터넷에서 쉽게 공부하기 좋은 곳)
  • [추천] deepcumen - Convolutional Neural Network ⁸
• [추천] Learning Deep Architecture for AI
• Famous' Archive 꿈꾸는 프로그래머 (머신러닝 한글 블로그 - SVM관련 내용이 좋다)
• [추천] 딥러닝을 처음 시작하는 분들을 위한 모두의 연구소 기술 블로그
  • [추천] 기계학습/머신러닝 기초 (Machine Learning Basics) ⁹
  • 내맘대로 이해하는 Deep Belief Network와 Restricted Boltzmann Machine
    • Original: A fast learning algorithm for deep belief nets (PDF)
• CS-449: Neural Networks (Introduction to Neural Networks)
• [확인요망] 모두를 위한 머신러닝/딥러닝 강의
• [추천] Deeplearning4j Documentation (ko)

References