๐Ÿšดโ€โ™‚๏ธ
TIL
  • MAIN
  • : TIL?
  • : WIL
  • : Plan
  • : Retrospective
    • 21Y
      • Wait a moment!
      • 9M 2W
      • 9M1W
      • 8M4W
      • 8M3W
      • 8M2W
      • 8M1W
      • 7M4W
      • 7M3W
      • 7M2W
      • 7M1W
      • 6M5W
      • 1H
    • ์ƒˆ์‚ฌ๋žŒ ๋˜๊ธฐ ํ”„๋กœ์ ํŠธ
      • 2ํšŒ์ฐจ
      • 1ํšŒ์ฐจ
  • TIL : ML
    • Paper Analysis
      • BERT
      • Transformer
    • Boostcamp 2st
      • [S]Data Viz
        • (4-3) Seaborn ์‹ฌํ™”
        • (4-2) Seaborn ๊ธฐ์ดˆ
        • (4-1) Seaborn ์†Œ๊ฐœ
        • (3-4) More Tips
        • (3-3) Facet ์‚ฌ์šฉํ•˜๊ธฐ
        • (3-2) Color ์‚ฌ์šฉํ•˜๊ธฐ
        • (3-1) Text ์‚ฌ์šฉํ•˜๊ธฐ
        • (2-3) Scatter Plot ์‚ฌ์šฉํ•˜๊ธฐ
        • (2-2) Line Plot ์‚ฌ์šฉํ•˜๊ธฐ
        • (2-1) Bar Plot ์‚ฌ์šฉํ•˜๊ธฐ
        • (1-3) Python๊ณผ Matplotlib
        • (1-2) ์‹œ๊ฐํ™”์˜ ์š”์†Œ
        • (1-1) Welcome to Visualization (OT)
      • [P]MRC
        • (2๊ฐ•) Extraction-based MRC
        • (1๊ฐ•) MRC Intro & Python Basics
      • [P]KLUE
        • (5๊ฐ•) BERT ๊ธฐ๋ฐ˜ ๋‹จ์ผ ๋ฌธ์žฅ ๋ถ„๋ฅ˜ ๋ชจ๋ธ ํ•™์Šต
        • (4๊ฐ•) ํ•œ๊ตญ์–ด BERT ์–ธ์–ด ๋ชจ๋ธ ํ•™์Šต
        • [NLP] ๋ฌธ์žฅ ๋‚ด ๊ฐœ์ฒด๊ฐ„ ๊ด€๊ณ„ ์ถ”์ถœ
        • (3๊ฐ•) BERT ์–ธ์–ด๋ชจ๋ธ ์†Œ๊ฐœ
        • (2๊ฐ•) ์ž์—ฐ์–ด์˜ ์ „์ฒ˜๋ฆฌ
        • (1๊ฐ•) ์ธ๊ณต์ง€๋Šฅ๊ณผ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ
      • [U]Stage-CV
      • [U]Stage-NLP
        • 7W Retrospective
        • (10๊ฐ•) Advanced Self-supervised Pre-training Models
        • (09๊ฐ•) Self-supervised Pre-training Models
        • (08๊ฐ•) Transformer (2)
        • (07๊ฐ•) Transformer (1)
        • 6W Retrospective
        • (06๊ฐ•) Beam Search and BLEU score
        • (05๊ฐ•) Sequence to Sequence with Attention
        • (04๊ฐ•) LSTM and GRU
        • (03๊ฐ•) Recurrent Neural Network and Language Modeling
        • (02๊ฐ•) Word Embedding
        • (01๊ฐ•) Intro to NLP, Bag-of-Words
        • [ํ•„์ˆ˜ ๊ณผ์ œ 4] Preprocessing for NMT Model
        • [ํ•„์ˆ˜ ๊ณผ์ œ 3] Subword-level Language Model
        • [ํ•„์ˆ˜ ๊ณผ์ œ2] RNN-based Language Model
        • [์„ ํƒ ๊ณผ์ œ] BERT Fine-tuning with transformers
        • [ํ•„์ˆ˜ ๊ณผ์ œ] Data Preprocessing
      • Mask Wear Image Classification
        • 5W Retrospective
        • Report_Level1_6
        • Performance | Review
        • DAY 11 : HardVoting | MultiLabelClassification
        • DAY 10 : Cutmix
        • DAY 9 : Loss Function
        • DAY 8 : Baseline
        • DAY 7 : Class Imbalance | Stratification
        • DAY 6 : Error Fix
        • DAY 5 : Facenet | Save
        • DAY 4 : VIT | F1_Loss | LrScheduler
        • DAY 3 : DataSet/Lodaer | EfficientNet
        • DAY 2 : Labeling
        • DAY 1 : EDA
        • 2_EDA Analysis
      • [P]Stage-1
        • 4W Retrospective
        • (10๊ฐ•) Experiment Toolkits & Tips
        • (9๊ฐ•) Ensemble
        • (8๊ฐ•) Training & Inference 2
        • (7๊ฐ•) Training & Inference 1
        • (6๊ฐ•) Model 2
        • (5๊ฐ•) Model 1
        • (4๊ฐ•) Data Generation
        • (3๊ฐ•) Dataset
        • (2๊ฐ•) Image Classification & EDA
        • (1๊ฐ•) Competition with AI Stages!
      • [U]Stage-3
        • 3W Retrospective
        • PyTorch
          • (10๊ฐ•) PyTorch Troubleshooting
          • (09๊ฐ•) Hyperparameter Tuning
          • (08๊ฐ•) Multi-GPU ํ•™์Šต
          • (07๊ฐ•) Monitoring tools for PyTorch
          • (06๊ฐ•) ๋ชจ๋ธ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ
          • (05๊ฐ•) Dataset & Dataloader
          • (04๊ฐ•) AutoGrad & Optimizer
          • (03๊ฐ•) PyTorch ํ”„๋กœ์ ํŠธ ๊ตฌ์กฐ ์ดํ•ดํ•˜๊ธฐ
          • (02๊ฐ•) PyTorch Basics
          • (01๊ฐ•) Introduction to PyTorch
      • [U]Stage-2
        • 2W Retrospective
        • DL Basic
          • (10๊ฐ•) Generative Models 2
          • (09๊ฐ•) Generative Models 1
          • (08๊ฐ•) Sequential Models - Transformer
          • (07๊ฐ•) Sequential Models - RNN
          • (06๊ฐ•) Computer Vision Applications
          • (05๊ฐ•) Modern CNN - 1x1 convolution์˜ ์ค‘์š”์„ฑ
          • (04๊ฐ•) Convolution์€ ๋ฌด์—‡์ธ๊ฐ€?
          • (03๊ฐ•) Optimization
          • (02๊ฐ•) ๋‰ด๋Ÿด ๋„คํŠธ์›Œํฌ - MLP (Multi-Layer Perceptron)
          • (01๊ฐ•) ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ณธ ์šฉ์–ด ์„ค๋ช… - Historical Review
        • Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] Multi-headed Attention Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] LSTM Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] CNN Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] Optimization Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] MLP Assignment
      • [U]Stage-1
        • 1W Retrospective
        • AI Math
          • (AI Math 10๊ฐ•) RNN ์ฒซ๊ฑธ์Œ
          • (AI Math 9๊ฐ•) CNN ์ฒซ๊ฑธ์Œ
          • (AI Math 8๊ฐ•) ๋ฒ ์ด์ฆˆ ํ†ต๊ณ„ํ•™ ๋ง›๋ณด๊ธฐ
          • (AI Math 7๊ฐ•) ํ†ต๊ณ„ํ•™ ๋ง›๋ณด๊ธฐ
          • (AI Math 6๊ฐ•) ํ™•๋ฅ ๋ก  ๋ง›๋ณด๊ธฐ
          • (AI Math 5๊ฐ•) ๋”ฅ๋Ÿฌ๋‹ ํ•™์Šต๋ฐฉ๋ฒ• ์ดํ•ดํ•˜๊ธฐ
          • (AI Math 4๊ฐ•) ๊ฒฝ์‚ฌํ•˜๊ฐ•๋ฒ• - ๋งค์šด๋ง›
          • (AI Math 3๊ฐ•) ๊ฒฝ์‚ฌํ•˜๊ฐ•๋ฒ• - ์ˆœํ•œ๋ง›
          • (AI Math 2๊ฐ•) ํ–‰๋ ฌ์ด ๋ญ์˜ˆ์š”?
          • (AI Math 1๊ฐ•) ๋ฒกํ„ฐ๊ฐ€ ๋ญ์˜ˆ์š”?
        • Python
          • (Python 7-2๊ฐ•) pandas II
          • (Python 7-1๊ฐ•) pandas I
          • (Python 6๊ฐ•) numpy
          • (Python 5-2๊ฐ•) Python data handling
          • (Python 5-1๊ฐ•) File / Exception / Log Handling
          • (Python 4-2๊ฐ•) Module and Project
          • (Python 4-1๊ฐ•) Python Object Oriented Programming
          • (Python 3-2๊ฐ•) Pythonic code
          • (Python 3-1๊ฐ•) Python Data Structure
          • (Python 2-4๊ฐ•) String and advanced function concept
          • (Python 2-3๊ฐ•) Conditionals and Loops
          • (Python 2-2๊ฐ•) Function and Console I/O
          • (Python 2-1๊ฐ•) Variables
          • (Python 1-3๊ฐ•) ํŒŒ์ด์ฌ ์ฝ”๋”ฉ ํ™˜๊ฒฝ
          • (Python 1-2๊ฐ•) ํŒŒ์ด์ฌ ๊ฐœ์š”
          • (Python 1-1๊ฐ•) Basic computer class for newbies
        • Assignment
          • [์„ ํƒ ๊ณผ์ œ 3] Maximum Likelihood Estimate
          • [์„ ํƒ ๊ณผ์ œ 2] Backpropagation
          • [์„ ํƒ ๊ณผ์ œ 1] Gradient Descent
          • [ํ•„์ˆ˜ ๊ณผ์ œ 5] Morsecode
          • [ํ•„์ˆ˜ ๊ณผ์ œ 4] Baseball
          • [ํ•„์ˆ˜ ๊ณผ์ œ 3] Text Processing 2
          • [ํ•„์ˆ˜ ๊ณผ์ œ 2] Text Processing 1
          • [ํ•„์ˆ˜ ๊ณผ์ œ 1] Basic Math
    • ๋”ฅ๋Ÿฌ๋‹ CNN ์™„๋ฒฝ ๊ฐ€์ด๋“œ - Fundamental ํŽธ
      • ์ข…ํ•ฉ ์‹ค์Šต 2 - ์บ๊ธ€ Plant Pathology(๋‚˜๋ฌด์žŽ ๋ณ‘ ์ง„๋‹จ) ๊ฒฝ์—ฐ ๋Œ€ํšŒ
      • ์ข…ํ•ฉ ์‹ค์Šต 1 - 120์ข…์˜ Dog Breed Identification ๋ชจ๋ธ ์ตœ์ ํ™”
      • ์‚ฌ์ „ ํ›ˆ๋ จ ๋ชจ๋ธ์˜ ๋ฏธ์„ธ ์กฐ์ • ํ•™์Šต๊ณผ ๋‹ค์–‘ํ•œ Learning Rate Scheduler์˜ ์ ์šฉ
      • Advanced CNN ๋ชจ๋ธ ํŒŒํ—ค์น˜๊ธฐ - ResNet ์ƒ์„ธ์™€ EfficientNet ๊ฐœ์š”
      • Advanced CNN ๋ชจ๋ธ ํŒŒํ—ค์น˜๊ธฐ - AlexNet, VGGNet, GoogLeNet
      • Albumentation์„ ์ด์šฉํ•œ Augmentation๊ธฐ๋ฒ•๊ณผ Keras Sequence ํ™œ์šฉํ•˜๊ธฐ
      • ์‚ฌ์ „ ํ›ˆ๋ จ CNN ๋ชจ๋ธ์˜ ํ™œ์šฉ๊ณผ Keras Generator ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์ดํ•ด
      • ๋ฐ์ดํ„ฐ ์ฆ๊ฐ•์˜ ์ดํ•ด - Keras ImageDataGenerator ํ™œ์šฉ
      • CNN ๋ชจ๋ธ ๊ตฌํ˜„ ๋ฐ ์„ฑ๋Šฅ ํ–ฅ์ƒ ๊ธฐ๋ณธ ๊ธฐ๋ฒ• ์ ์šฉํ•˜๊ธฐ
    • AI School 1st
    • ํ˜„์—… ์‹ค๋ฌด์ž์—๊ฒŒ ๋ฐฐ์šฐ๋Š” Kaggle ๋จธ์‹ ๋Ÿฌ๋‹ ์ž…๋ฌธ
    • ํŒŒ์ด์ฌ ๋”ฅ๋Ÿฌ๋‹ ํŒŒ์ดํ† ์น˜
  • TIL : Python & Math
    • Do It! ์žฅ๊ณ +๋ถ€ํŠธ์ŠคํŠธ๋žฉ: ํŒŒ์ด์ฌ ์›น๊ฐœ๋ฐœ์˜ ์ •์„
      • Relations - ๋‹ค๋Œ€๋‹ค ๊ด€๊ณ„
      • Relations - ๋‹ค๋Œ€์ผ ๊ด€๊ณ„
      • ํ…œํ”Œ๋ฆฟ ํŒŒ์ผ ๋ชจ๋“ˆํ™” ํ•˜๊ธฐ
      • TDD (Test Driven Development)
      • template tags & ์กฐ๊ฑด๋ฌธ
      • ์ •์  ํŒŒ์ผ(static files) & ๋ฏธ๋””์–ด ํŒŒ์ผ(media files)
      • FBV (Function Based View)์™€ CBV (Class Based View)
      • Django ์ž…๋ฌธํ•˜๊ธฐ
      • ๋ถ€ํŠธ์ŠคํŠธ๋žฉ
      • ํ”„๋ก ํŠธ์—”๋“œ ๊ธฐ์ดˆ๋‹ค์ง€๊ธฐ (HTML, CSS, JS)
      • ๋“ค์–ด๊ฐ€๊ธฐ + ํ™˜๊ฒฝ์„ค์ •
    • Algorithm
      • Programmers
        • Level1
          • ์†Œ์ˆ˜ ๋งŒ๋“ค๊ธฐ
          • ์ˆซ์ž ๋ฌธ์ž์—ด๊ณผ ์˜๋‹จ์–ด
          • ์ž์—ฐ์ˆ˜ ๋’ค์ง‘์–ด ๋ฐฐ์—ด๋กœ ๋งŒ๋“ค๊ธฐ
          • ์ •์ˆ˜ ๋‚ด๋ฆผ์ฐจ์ˆœ์œผ๋กœ ๋ฐฐ์น˜ํ•˜๊ธฐ
          • ์ •์ˆ˜ ์ œ๊ณฑ๊ทผ ํŒ๋ณ„
          • ์ œ์ผ ์ž‘์€ ์ˆ˜ ์ œ๊ฑฐํ•˜๊ธฐ
          • ์ง์‚ฌ๊ฐํ˜• ๋ณ„์ฐ๊ธฐ
          • ์ง์ˆ˜์™€ ํ™€์ˆ˜
          • ์ฒด์œก๋ณต
          • ์ตœ๋Œ€๊ณต์•ฝ์ˆ˜์™€ ์ตœ์†Œ๊ณต๋ฐฐ์ˆ˜
          • ์ฝœ๋ผ์ธ  ์ถ”์ธก
          • ํฌ๋ ˆ์ธ ์ธํ˜•๋ฝ‘๊ธฐ ๊ฒŒ์ž„
          • ํ‚คํŒจ๋“œ ๋ˆ„๋ฅด๊ธฐ
          • ํ‰๊ท  ๊ตฌํ•˜๊ธฐ
          • ํฐ์ผ“๋ชฌ
          • ํ•˜์ƒค๋“œ ์ˆ˜
          • ํ•ธ๋“œํฐ ๋ฒˆํ˜ธ ๊ฐ€๋ฆฌ๊ธฐ
          • ํ–‰๋ ฌ์˜ ๋ง์…ˆ
        • Level2
          • ์ˆซ์ž์˜ ํ‘œํ˜„
          • ์ˆœ์œ„ ๊ฒ€์ƒ‰
          • ์ˆ˜์‹ ์ตœ๋Œ€ํ™”
          • ์†Œ์ˆ˜ ์ฐพ๊ธฐ
          • ์†Œ์ˆ˜ ๋งŒ๋“ค๊ธฐ
          • ์‚ผ๊ฐ ๋‹ฌํŒฝ์ด
          • ๋ฌธ์ž์—ด ์••์ถ•
          • ๋ฉ”๋‰ด ๋ฆฌ๋‰ด์–ผ
          • ๋” ๋งต๊ฒŒ
          • ๋•…๋”ฐ๋จน๊ธฐ
          • ๋ฉ€์ฉกํ•œ ์‚ฌ๊ฐํ˜•
          • ๊ด„ํ˜ธ ํšŒ์ „ํ•˜๊ธฐ
          • ๊ด„ํ˜ธ ๋ณ€ํ™˜
          • ๊ตฌ๋ช…๋ณดํŠธ
          • ๊ธฐ๋Šฅ ๊ฐœ๋ฐœ
          • ๋‰ด์Šค ํด๋Ÿฌ์Šคํ„ฐ๋ง
          • ๋‹ค๋ฆฌ๋ฅผ ์ง€๋‚˜๋Š” ํŠธ๋Ÿญ
          • ๋‹ค์Œ ํฐ ์ˆซ์ž
          • ๊ฒŒ์ž„ ๋งต ์ตœ๋‹จ๊ฑฐ๋ฆฌ
          • ๊ฑฐ๋ฆฌ๋‘๊ธฐ ํ™•์ธํ•˜๊ธฐ
          • ๊ฐ€์žฅ ํฐ ์ •์‚ฌ๊ฐํ˜• ์ฐพ๊ธฐ
          • H-Index
          • JadenCase ๋ฌธ์ž์—ด ๋งŒ๋“ค๊ธฐ
          • N๊ฐœ์˜ ์ตœ์†Œ๊ณต๋ฐฐ์ˆ˜
          • N์ง„์ˆ˜ ๊ฒŒ์ž„
          • ๊ฐ€์žฅ ํฐ ์ˆ˜
          • 124 ๋‚˜๋ผ์˜ ์ˆซ์ž
          • 2๊ฐœ ์ดํ•˜๋กœ ๋‹ค๋ฅธ ๋น„ํŠธ
          • [3์ฐจ] ํŒŒ์ผ๋ช… ์ •๋ ฌ
          • [3์ฐจ] ์••์ถ•
          • ์ค„ ์„œ๋Š” ๋ฐฉ๋ฒ•
          • [3์ฐจ] ๋ฐฉ๊ธˆ ๊ทธ๊ณก
          • ๊ฑฐ๋ฆฌ๋‘๊ธฐ ํ™•์ธํ•˜๊ธฐ
        • Level3
          • ๋งค์นญ ์ ์ˆ˜
          • ์™ธ๋ฒฝ ์ ๊ฒ€
          • ๊ธฐ์ง€๊ตญ ์„ค์น˜
          • ์ˆซ์ž ๊ฒŒ์ž„
          • 110 ์˜ฎ๊ธฐ๊ธฐ
          • ๊ด‘๊ณ  ์ œ๊ฑฐ
          • ๊ธธ ์ฐพ๊ธฐ ๊ฒŒ์ž„
          • ์…”ํ‹€๋ฒ„์Šค
          • ๋‹จ์†์นด๋ฉ”๋ผ
          • ํ‘œ ํŽธ์ง‘
          • N-Queen
          • ์ง•๊ฒ€๋‹ค๋ฆฌ ๊ฑด๋„ˆ๊ธฐ
          • ์ตœ๊ณ ์˜ ์ง‘ํ•ฉ
          • ํ•ฉ์Šน ํƒ์‹œ ์š”๊ธˆ
          • ๊ฑฐ์Šค๋ฆ„๋ˆ
          • ํ•˜๋…ธ์ด์˜ ํƒ‘
          • ๋ฉ€๋ฆฌ ๋›ฐ๊ธฐ
          • ๋ชจ๋‘ 0์œผ๋กœ ๋งŒ๋“ค๊ธฐ
        • Level4
    • Head First Python
    • ๋ฐ์ดํ„ฐ ๋ถ„์„์„ ์œ„ํ•œ SQL
    • ๋‹จ ๋‘ ์žฅ์˜ ๋ฌธ์„œ๋กœ ๋ฐ์ดํ„ฐ ๋ถ„์„๊ณผ ์‹œ๊ฐํ™” ๋ฝ€๊ฐœ๊ธฐ
    • Linear Algebra(Khan Academy)
    • ์ธ๊ณต์ง€๋Šฅ์„ ์œ„ํ•œ ์„ ํ˜•๋Œ€์ˆ˜
    • Statistics110
  • TIL : etc
    • [๋”ฐ๋ฐฐ๋Ÿฐ] Kubernetes
    • [๋”ฐ๋ฐฐ๋Ÿฐ] Docker
      • 2. ๋„์ปค ์„ค์น˜ ์‹ค์Šต 1 - ํ•™์ŠตํŽธ(์ค€๋น„๋ฌผ/์‹ค์Šต ์œ ํ˜• ์†Œ๊ฐœ)
      • 1. ์ปจํ…Œ์ด๋„ˆ์™€ ๋„์ปค์˜ ์ดํ•ด - ์ปจํ…Œ์ด๋„ˆ๋ฅผ ์“ฐ๋Š”์ด์œ  / ์ผ๋ฐ˜ํ”„๋กœ๊ทธ๋žจ๊ณผ ์ปจํ…Œ์ด๋„ˆํ”„๋กœ๊ทธ๋žจ์˜ ์ฐจ์ด์ 
      • 0. ๋“œ๋””์–ด ์ฐพ์•„์˜จ Docker ๊ฐ•์˜! ์™•์ดˆ๋ณด์—์„œ ๋„์ปค ๋งˆ์Šคํ„ฐ๋กœ - OT
    • CoinTrading
      • [๊ฐ€์ƒ ํ™”ํ ์ž๋™ ๋งค๋งค ํ”„๋กœ๊ทธ๋žจ] ๋ฐฑํ…Œ์ŠคํŒ… : ๊ฐ„๋‹จํ•œ ํ…Œ์ŠคํŒ…
    • Gatsby
      • 01 ๊นƒ๋ถ ํฌ๊ธฐ ์„ ์–ธ
  • TIL : Project
    • Mask Wear Image Classification
    • Project. GARIGO
  • 2021 TIL
    • CHANGED
    • JUN
      • 30 Wed
      • 29 Tue
      • 28 Mon
      • 27 Sun
      • 26 Sat
      • 25 Fri
      • 24 Thu
      • 23 Wed
      • 22 Tue
      • 21 Mon
      • 20 Sun
      • 19 Sat
      • 18 Fri
      • 17 Thu
      • 16 Wed
      • 15 Tue
      • 14 Mon
      • 13 Sun
      • 12 Sat
      • 11 Fri
      • 10 Thu
      • 9 Wed
      • 8 Tue
      • 7 Mon
      • 6 Sun
      • 5 Sat
      • 4 Fri
      • 3 Thu
      • 2 Wed
      • 1 Tue
    • MAY
      • 31 Mon
      • 30 Sun
      • 29 Sat
      • 28 Fri
      • 27 Thu
      • 26 Wed
      • 25 Tue
      • 24 Mon
      • 23 Sun
      • 22 Sat
      • 21 Fri
      • 20 Thu
      • 19 Wed
      • 18 Tue
      • 17 Mon
      • 16 Sun
      • 15 Sat
      • 14 Fri
      • 13 Thu
      • 12 Wed
      • 11 Tue
      • 10 Mon
      • 9 Sun
      • 8 Sat
      • 7 Fri
      • 6 Thu
      • 5 Wed
      • 4 Tue
      • 3 Mon
      • 2 Sun
      • 1 Sat
    • APR
      • 30 Fri
      • 29 Thu
      • 28 Wed
      • 27 Tue
      • 26 Mon
      • 25 Sun
      • 24 Sat
      • 23 Fri
      • 22 Thu
      • 21 Wed
      • 20 Tue
      • 19 Mon
      • 18 Sun
      • 17 Sat
      • 16 Fri
      • 15 Thu
      • 14 Wed
      • 13 Tue
      • 12 Mon
      • 11 Sun
      • 10 Sat
      • 9 Fri
      • 8 Thu
      • 7 Wed
      • 6 Tue
      • 5 Mon
      • 4 Sun
      • 3 Sat
      • 2 Fri
      • 1 Thu
    • MAR
      • 31 Wed
      • 30 Tue
      • 29 Mon
      • 28 Sun
      • 27 Sat
      • 26 Fri
      • 25 Thu
      • 24 Wed
      • 23 Tue
      • 22 Mon
      • 21 Sun
      • 20 Sat
      • 19 Fri
      • 18 Thu
      • 17 Wed
      • 16 Tue
      • 15 Mon
      • 14 Sun
      • 13 Sat
      • 12 Fri
      • 11 Thu
      • 10 Wed
      • 9 Tue
      • 8 Mon
      • 7 Sun
      • 6 Sat
      • 5 Fri
      • 4 Thu
      • 3 Wed
      • 2 Tue
      • 1 Mon
    • FEB
      • 28 Sun
      • 27 Sat
      • 26 Fri
      • 25 Thu
      • 24 Wed
      • 23 Tue
      • 22 Mon
      • 21 Sun
      • 20 Sat
      • 19 Fri
      • 18 Thu
      • 17 Wed
      • 16 Tue
      • 15 Mon
      • 14 Sun
      • 13 Sat
      • 12 Fri
      • 11 Thu
      • 10 Wed
      • 9 Tue
      • 8 Mon
      • 7 Sun
      • 6 Sat
      • 5 Fri
      • 4 Thu
      • 3 Wed
      • 2 Tue
      • 1 Mon
    • JAN
      • 31 Sun
      • 30 Sat
      • 29 Fri
      • 28 Thu
      • 27 Wed
      • 26 Tue
      • 25 Mon
      • 24 Sun
      • 23 Sat
      • 22 Fri
      • 21 Thu
      • 20 Wed
      • 19 Tue
      • 18 Mon
      • 17 Sun
      • 16 Sat
      • 15 Fri
      • 14 Thu
      • 13 Wed
      • 12 Tue
      • 11 Mon
      • 10 Sun
      • 9 Sat
      • 8 Fri
      • 7 Thu
      • 6 Wed
      • 5 Tue
      • 4 Mon
      • 3 Sun
      • 2 Sat
      • 1 Fri
  • 2020 TIL
    • DEC
      • 31 Thu
      • 30 Wed
      • 29 Tue
      • 28 Mon
      • 27 Sun
      • 26 Sat
      • 25 Fri
      • 24 Thu
      • 23 Wed
      • 22 Tue
      • 21 Mon
      • 20 Sun
      • 19 Sat
      • 18 Fri
      • 17 Thu
      • 16 Wed
      • 15 Tue
      • 14 Mon
      • 13 Sun
      • 12 Sat
      • 11 Fri
      • 10 Thu
      • 9 Wed
      • 8 Tue
      • 7 Mon
      • 6 Sun
      • 5 Sat
      • 4 Fri
      • 3 Tue
      • 2 Wed
      • 1 Tue
    • NOV
      • 30 Mon
Powered by GitBook
On this page
  • ๋ฐ์ดํ„ฐ ๋ถ„์„์„ ์œ„ํ•œ ๊ธฐ์ดˆ SQL
  • OT
  • ๋ณด๊ณ  ์‹ถ์€ ๋ฐ์ดํ„ฐ ๊บผ๋‚ด์˜ค๊ธฐ
  • ์กฐ๊ฑด์— ๋งž๋Š” ๋ฐ์ดํ„ฐ ๊ฒ€์ƒ‰ํ•˜๊ธฐ

Was this helpful?

  1. 2021 TIL
  2. JAN

29 Fri

TIL

๋ฐ์ดํ„ฐ ๋ถ„์„์„ ์œ„ํ•œ ๊ธฐ์ดˆ SQL

OT

๊ฐ•์ขŒ ์†Œ๊ฐœ

  • SELECT - ์งˆ์˜์–ด

  • INSERT, UPDATE, DELETE - ์กฐ์ž‘์–ด

๊ฐ•์˜ ์ˆ˜๊ฐ•์„ ์œ„ํ•œ ์ค€๋น„ & ๋งํฌ ์•ˆ๋‚ด

  • ํ•ด์ปค๋žญํฌ ๊ฐ€์ž… -> ๋ฌธ์ œ ํ’€์ด๋ฅผ ์œ„ํ•จ

  • w3school -> ์‹ค์Šต

๋ณด๊ณ  ์‹ถ์€ ๋ฐ์ดํ„ฐ ๊บผ๋‚ด์˜ค๊ธฐ

SELECT / FROM /LIMIT

  • ๋ฐ์ดํ„ฐ๋Š” ํ‘œ๋กœ ๋‚˜ํƒ€๋‚ด๋ฉฐ ๊ฐ€๋กœ๋Š” row, ์„ธ๋กœ๋Š” column

  • ๊ฐ€๋กœ๋Š” ๋ฐ์ดํ„ฐ ํ•œ ๊ฐœ๋ฅผ ์˜๋ฏธํ•˜๋ฉฐ ์„ธ๋กœ๋Š” ๋ฐ์ดํ„ฐ์˜ ํŠน์„ฑ์„ ์˜๋ฏธ

SELECT * FROM Customers;
  • FROM : ์–ด๋–ค ํ…Œ์ด๋ธ”์—์„œ ๋ฐ์ดํ„ฐ๋ฅผ ๊ฐ€์ ธ์˜ฌ ์ง€

  • SELECT : ๋ฌด์—‡์„ ๊ฐ€์ ธ์˜ฌ์ง€

  • *: ์ „์ฒด ๋‚ด์šฉ์„ ๊ฐ€์ง€๊ณ  ์˜ค๋ผ๋Š” ๋‹จ์ถ•์–ด(asterisk)

SELECT CustomerName, Address FROM Customers LIMIT 10;
  • SELECT ๋’ค์— ํ•„๋“œ ๋ช…์„ ์ž…๋ ฅํ•˜๋ฉด ๋˜๊ณ  ์—ฌ๋Ÿฌ๊ฐœ์˜ ํ•„๋“œ ๋ช…์€ ์ฝค๋งˆ๋กœ ๊ตฌ๋ถ„ ๊ฐ€๋Šฅ

  • LIMIT : ๋ฐ์ดํ„ฐ๋ฅผ N๊ฐœ๋งŒ ๋ฝ‘์•„ ์˜ค๋Š” ๋ฐฉ๋ฒ•

    • ๋ฐ์ดํ„ฐ๊ฐ€ ๋„ˆ๋ฌด ๋งŽ์œผ๋ฉด ๋งŽ์€ ์ •๋ณด๋ฅผ ์ถœ๋ ฅํ•ด์•ผ ํ•˜๋ฉฐ ๋งŽ์€ ์‹œ๊ฐ„์ด ์†Œ์š”๋œ๋‹ค.

    • ๋ฐ์ดํ„ฐ์˜ ๊ตฌ์กฐ๋ฅผ ๊ฐ„๋‹จํ•˜๊ฒŒ ํŒŒ์•…ํ•  ๋•Œ ์‚ฌ์šฉ ๊ฐ€๋Šฅ

  • SQL์—์„œ ๋Œ€์†Œ๋ฌธ์ž ๊ตฌ๋ณ„์— ๋Œ€ํ•œ ๊ฐ•์ œ๋Š” ์กด์žฌํ•˜์ง€ ์•Š์ง€๋งŒ, ๊ฐ€๋…์„ฑ์„ ์œ„ํ•ด ์˜ˆ์•ฝ์–ด๋Š” ๋Œ€๋ฌธ์ž๋กœ, ๋‚˜๋จธ์ง€ ๋ฌธ์ž๋“ค์€ ์†Œ๋ฌธ์ž๋กœ ์“ฐ๋Š”๊ฒƒ์ด ๊ถŒ์žฅ๋œ๋‹ค.

์กฐ๊ฑด์— ๋งž๋Š” ๋ฐ์ดํ„ฐ ๊ฒ€์ƒ‰ํ•˜๊ธฐ

๋น„๊ต์—ฐ์‚ฐ์ž์™€ ๋…ผ๋ฆฌ์—ฐ์‚ฐ์ž

  • ์‹ค์ œ๋กœ ๋ฐ์ดํ„ฐ๋ฅผ ๋ชจ๋‘ ๋ณผ ์ผ์€ ์—†์Œ

    • ID๊ฐ€ 30๋ฒˆ ์ด์ƒ์ด๊ฑฐ๋‚˜ VALUE๊ฐ€ 5 ์ด์ƒ์ธ ๋ฐ์ดํ„ฐ๋งŒ์„ ๋ณด๋Š” ๋“ฑ์˜ ์กฐ๊ฑด๋ถ€ ๋ฐ์ดํ„ฐ ๊ด€์ฐฐ์„ ๋งŽ์ดํ•จ

SELECT *
From Customers
WHERE Country = 'Germany'
  • WHERE : ํŠน์ • ์ปฌ๋Ÿผ๋งŒ์„ ๊ฐ€์ง€๊ณ  ์˜ค๊ธฐ ์œ„ํ•ด์„œ ์‚ฌ์šฉ

    • EX

      • Customers < "B"

      • Country = 'Germany'

      • CustomerID = 5 AND City = 'Berlin

    • WHERE ๊ตฌ๋ฌธ์—์„œ AND ๋˜๋Š” OR ์—ฐ์‚ฐ์„ ์‚ฌ์šฉ ๊ฐ€๋Šฅ

LIKE, IN, BETWEEN, IS NULL

SELECT *
From Customers
WHERE Country LIKE'%r%'
  • LIKE : ๋ฌธ์ž์—ด์˜ ํŒจํ„ด์„ ๊ฐ€์ง€๊ณ  ๊ฒ€์ƒ‰์„ ํ•  ๋•Œ ์‚ฌ์šฉํ•œ๋‹ค

    • EX

      • LIKE '%r%' : ์ค‘๊ฐ„์— 'r'์ด ๋“ค์–ด๊ฐ€๋Š” ๋ฌธ์ž์—ด

      • LIKE 'br%' : 'br' ๋กœ ์‹œ์ž‘ํ•˜๋Š” ๋ฌธ์ž์—ด

  • IN : ๊ตฌ๋ฌธ์ด ๋„ˆ๋ฌด ๊ธธ์–ด์งˆ ๋•Œ ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•˜๋‹ค

    • Counttry IN ('Germany', 'France', 'Korea')

  • BETWEEN : ๋ฒ”์œ„ํ˜• ์ง‘ํ•ฉ์„ ์ •์˜ํ•  ๋•Œ ์‚ฌ์šฉํ•œ๋‹ค.

    • CustomerID BETWEEN 3 AND 5

    • ์ˆซ์ž, ๋ฌธ์ž์—ด, ๋‚ ์งœ ๋ฐ์ดํ„ฐ ๋“ฑ ๊ฐ€๋Šฅํ•˜๋‹ค.

    • ์‹œ์ž‘๊ฐ’๊ณผ ๋๊ฐ’๋„ ํฌํ•จํ•œ๋‹ค.

  • ISNULL : ๋น„์–ด์žˆ๋Š” ๊ฐ’์„ ๋‚˜ํƒ€๋‚ด๋ฉฐ ๋น„๊ต์—ฐ์‚ฐ์ž๋กœ ๋น„๊ตํ•  ์ˆ˜๋Š” ์—†๋‹ค.

    • CustomerID != NULL (X)

    • CustomerID IS NULL

    • CustomerID IS NOT NULL

LIKE ์‹ฌํ™”

SELECT *
From Customers
WHERE Country LIKE'br%'
  • % : ์–ด๋–ค ๋ฌธ์ž์—ด๊ณผ๋„ ๋งค์นญ๋˜๋ฉฐ ์ด ๋ฌธ์ž๋ฅผ ์™€์ผ๋“œ์นด๋“œ๋ผ๊ณ  ๋ถ€๋ฅธ๋‹ค.

  • = ์ด LIKE ๋ณด๋‹ค ๋น ๋ฅด๋‹ค

    • ๊ฒ€์ƒ‰ํ•˜๊ณ ์ž ํ•˜๋Š” ํ‚ค์›Œ๋“œ๊ฐ€ ๋ช…๋ฃŒํ•˜์ž๋ฉด = ์„ ์‚ฌ์šฉํ•  ๊ฒƒ.

  • LIKE 'B_____'

    • B๋กœ ์‹œ์ž‘ํ•œ ๋‹ค์Œ์— 5๊ฐœ์˜ ๋ฌธ์ž๊ฐ€ ๋”ฐ๋ผ์˜จ๋‹ค๋Š” ์˜๋ฏธ. ๋ฌธ์ž์˜ ๊ฐœ์ˆ˜์™€ ์–ธ๋”๋ฐ”์˜ ๊ฐœ์ˆ˜์™€ ๋™์ผํ•˜๋‹ค.

    • ์ด์™€ ๊ฐ™์€ ๊ฒฝ์šฐ๋Š” Brazil์€ ์ฐพ์•„์ง€์ง€๋งŒ Belgium์€ ์ฐพ์•„์ง€์ง€ ์•Š๋Š”๋‹ค.

  • string %

    • ์™€์ผ๋“œ์นด๋“œ๊ฐ€ ์•„๋‹Œ ์‹ค์ œ ํผ์„ผํŠธ๋ฅผ ์ฐพ๊ณ  ์‹ถ๋‹ค๋ฉด \% ๊ณผ ๊ฐ™์ด ํ‘œํ˜„ํ•˜๋ฉด ๋œ๋‹ค.

  • ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค

    • postgresql

    • mssql

    • redshift

    • ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์—์„œ ๋ฌธ๋ฒ•์„ ๊ฒ€์ƒ‰ํ•  ๋•Œ๋Š” ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ์†Œํ”„ํŠธ์›จ์–ด ์ด๋ฆ„๊ณผ ๋ฌธ๋ฒ•์„ ๊ฒ€์ƒ‰ํ•  ๊ฒƒ

    • EX) redshift LIKE

  • SELECT DISTINCT city

    • city์˜ ํ…Œ์ด๋ธ” ๊ฐ’์„ ์ค‘๋ณต๋˜๋Š” ๊ฐ’์ด ์—†๊ฒŒ ์ถœ๋ ฅํ•ด์„œ ๋ณด์—ฌ์ฃผ๋ผ๋Š” ์˜๋ฏธ

  • RLIKE

    • ์—ฌ๋Ÿฌ๊ฐœ์˜ ์กฐ๊ฑด์ด ํ•„์š”ํ•  ๋•Œ LIKE ๊ฐ™์€ ๊ฒฝ์šฐ์—๋Š” LIKE regex OR ์„ ๋ฐ˜๋ณต์ ์œผ๋กœ ์จ์•ผ ํ•œ๋‹ค.

    • RLIKE๋ฅผ ์‚ฌ์šฉํ•˜๊ฒŒ ๋˜๋ฉด RLIKE 'regex | regex' ์™€ ๊ฐ™์€ ๊ผด๋กœ ์“ธ ์ˆ˜ ์žˆ๋‹ค.

Previous30 SatNext28 Thu

Last updated 4 years ago

Was this helpful?