ML_study_note

This repository is to organize what I learned about machine learning

How to start

• how to read papers / Summary
  • Just see Title-Abstrat-Experiment categories of the paper
  • When seeing experiment, focus on the graphs, images
• advice by Andrew Ng
• How to use Colab

Machine Learning Basics

• Youtube video that explains mathematical concepts about ml

Concepts

• classification problems vs regression problems (summary)
• supervised learning vs unsupervised learning (summary)

Models

• preprocessing

Supervised Learning

• regression
  • linear regression
• classification
  • Naive Bayes Classification
  • k-nearest neighbor classification
  • Decision Tree classification
  • Random Forest classification

Unsupervised Learning

• clustering
  • [K-means clustering]
  • [Heirachical clustering]

Tools

• numpy (tutorial)
• pandas (tutorial)
• matplotlib.pyplot (tutorial)
• sklearn (tutorial)

Image processing

academic papers

• overview

• R-CNN(Explanation)
• DETR(Explanation)
  • pros
    • Doesn't need hand-designed components
    • Doesn't need specialized library

Terms

• IoU
• Non-Max Suppression, NMS
• mean Average Precision, mAP/ Precision/ Recall
• crop, warp

NLP

How to write paper

• Advice by Sue Moon
• Advice by 화난 에르빈 슈뢰딩거

Reference

• Deep Learning Paper review blog
• Deep Learning Paper review github
• Deep Learning Paper review by Na Dong Bin
• How to study Machine Learning
• my notion