코세라 Deep Learning 정리

Deep Learning - Coursera

 

 

1. Neural Networks and Deep Learning
   
   1. WEEK1 : introduction to Neural Network
   2. WEEK2 : activation function (활성화함수)
2. Improving Deep Neural Networks
   1. WEEK3 : train test split (with bias)
   2. WEEK3 : regularization (정규화)
   3. WEEK3 : weight initialization (가중치 초기화)
   4. WEEK4 : Optimizer (최적화 알고리즘)
   5. WEEK4 : problem of optimization
   6. WEEK4 : batch normalization (배치 정규화)
3. Structuring Machine Learning Projects
   1. WEEK5 : Machine Learning Strategy
   2. WEEK5 : ML 모델의 목표 설정하고 달성하기
   3. WEEK5 : avoidable bias & variance 비교를 통한 전략 세우기
   4. WEEK5 : error analysis (에러 분석)
   5. WEEK5 : traning set과 dev/test set의 불일치
   6. WEEK5 : Transfer Learning
   7. WEEK5 : Multi-Task Learning
   8. WEEK5 : end to end DL
4. Convolutional Neural Networks
   1. WEEK5 : CNN (convolutional neural network)
   2. WEEK6 : ResNet
   3. WEEK6 : Inception (googLeNet)
   4. WEEK6 : convNet 사용에 도움이 될 지식
   5. WEEK6 : Object Detection (1)
   6. WEEK6 : Object Detection (2)
   7. WEEK6 : R-CNN
   8. WEEK7 : face recognition
   9. WEEK7 : Neural Style Transfer
   10. WEEK7 : convNet in 1D, 2D, 3D
5. Sequence Models
   1. WEEK7 : RNN
   2. WEEK7 : LSTM, GRU
   3. WEEK8 : Word Embedding (word2vec)
   4. WEEK8 : negative sampling
   5. WEEK8 : beam search in language model
   6. WEEK8 : Bleu score
   7. WEEK8 : Attention