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- 1: 자료 모으고 조직하기
Gathering and Organizing Data- 고개발자 스타일로 자료 다루기
Handling data with Gopher style - 고로 자료를 모으고 조직하는 최고의 방법
Best practices for gathering and organizing data with Go - 쉼표 분리 값 파일
CSV files - JSON
자바스크립트 객체 표기법 파일 - SQL_like databases
SQL 같은 데이터베이스 - Caching
캐싱 - Data versioning
데이터 버저닝 - References
- Summary
- 고개발자 스타일로 자료 다루기
- 2: 행렬, 확률 그리고 통계
Matrices, Probability, and Statistics- 행렬과 벡터
Matrices and vectors - 통계
Statistics - 확률
Probability - References
- Summary
- 행렬과 벡터
- 3: 평가와 검증
Evaluation and Validation- 평가
Evaluation - 검증
Validation - References
- Summary
- 평가
- 4: 회귀
Regression- 회귀 모델이라는 전문 용어 이해하기 Understanding regression model jargon
- 선형 회귀
Linear regression - 다중 선형 회귀
Multiple linear regression - 비선형 혹은 다른 회귀 종류
Nonlinear and other types of regression - References
- Summary
- 5: 분류
Classification- Understanding classification model jargon
- Logistic regression
- k_nearest neighbors
- Decision trees and random forests
- Naive bayes
- References
- Summary
- 6: Clustering
- Understanding clustering model jargon
- Measuring Distance or Similarity
- Evaluating clustering techniques
- k_means clustering
- Other clustering techniques
- References
- Summary
- 7: Time Series and Anomaly Detection
- Representing time series data in Go
- Understanding time series jargon
- Statistics related to time series
- Auto_regressive models for forecasting
- Auto_regressive moving averages and other time series models
- Anomaly detection
- References
- Summary
- 8: Neural Networks and Deep Learning
- Understanding neural net jargon
- Building a simple neural network
- Utilizing the simple neural network
- Introducing deep learning
- References
- Summary
- 9: Deploying and Distributing Analyses and Models
- Running models reliably on remote machines
- Building a scalable and reproducible machine learning pipeline
- References
- Summary
- 10: Algorithms/Techniques Related to Machine Learning
- Gradient descent
- Entropy, information gain, and related methods
- Backpropagation