/Machine-Learning-With-Go

Machine Learning With Go, published by Packt

Primary LanguageJupyter NotebookMIT LicenseMIT

브라우저에서 쥬피터 노트북 보기

Binder

go 커널은 바인더에서 지원하지 않습니다. 그냥 커널 없이 결과물만 보세요.

Progress

First phase

  • 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
  • 2: 행렬, 확률 그리고 통계
    Matrices, Probability, and Statistics
    • 행렬과 벡터
      Matrices and vectors
    • 통계
      Statistics
    • 확률
      Probability
    • References
    • Summary

Second phase

  • 3: 평가와 검증
    Evaluation and Validation
    • 평가
      Evaluation
    • 검증
      Validation
    • References
    • Summary
  • 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

Third phase

  • 5: 분류
    Classification
    • Understanding classification model jargon
    • Logistic regression
    • k_nearest neighbors
    • Decision trees and random forests
    • Naive bayes
    • References
    • Summary

Fourth phase - I'm not sure.

  • 6: Clustering
    • Understanding clustering model jargon
    • Measuring Distance or Similarity
    • Evaluating clustering techniques
    • k_means clustering
    • Other clustering techniques
    • References
    • Summary

Fifth phase - I'm not sure.

  • 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

I'm not sure.

  • 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