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카테고리
Structure of Deep Neural Network
01-28
Generalization for Linear Models
01-27
Non-linear Data 처리 방법
01-27
Data Preprocessing Concepts
01-26
선형 모델의 Overfitting과 Regularization
01-24
선형 모델을 위한 noise-robust 학습 방법론
01-24
GloVe & NLP for Stock Price Prediction
01-23
Word2Vec for Finance
01-20
Linear Regressor vs. Linear Classifier
01-19
GloVe & NLP for Stock Price Prediction
01-23
Word2Vec for Finance
01-20
Write Code Every Day
01-21
네이버 블로그 리뷰 크롤링
01-22
데이터 결측치 채우는 6가지 방법
02-18
Non-linear Data 처리 방법
01-27
Data Preprocessing Concepts
01-26
선형 모델의 Overfitting과 Regularization
01-24
선형 모델을 위한 noise-robust 학습 방법론
01-24
Structure of Deep Neural Network
01-28
Bias and Variance
03-12
AI 프로젝트 아이디어 평가를 위한 5단계 기준
03-08
Takeaways for Basic error analysis
02-22
How big should the Eyeball and Blackbox dev sets be?
02-22
Cleaning up mislabeled dev and test set examples
02-17
Evaluating multiple ideas in parallel during error analysis
02-16
Error analysis - Look at dev set examples to evaluate ideas
02-15
Build your first system quickly, then iterate
02-14
Takeaways for setting up development and test sets
02-11
When to change dev/test sets and metrics
02-10
Having a dev set and metric speeds up iterations
02-10
Establish a single-number evaluation metric for your team to optimize
02-09
Your dev and test sets should come from the same distribution
02-08
How large do the dev/test sets need to be?
02-07
Your development and test sets
02-06
Scale drives machine learning progress
02-05
Prerequisites and Notation
02-04
How to use this book to help your team
02-03
Why Machine Learning Strategy
02-02
위기에 빠진 구글 AI 그리고 비명세성 문제
03-09