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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)
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  • ๊ฐ€์ƒํ™”ํ ํˆฌ์ž๊ณต์‹
  • PART4. ๊ฐ€์ƒํ™”ํ ์‹ค์ „ ํˆฌ์ž ์ „๋žต -1
  • ํˆฌ์ž ์ „๋žต 1. ๊ฐ€์ƒํ™”ํ 10% + ํ˜„๊ธˆ 90%
  • ํˆฌ์ž ์ „๋žต 2. ์ด๋™ํ‰๊ท  + ํ˜„๊ธˆ ๋น„์ค‘ 80%์ด์ƒ
  • ํˆฌ์ž ์ „๋žต 3. ์Šˆํผ ์ƒ์Šน์žฅ + ๋ณ€๋™์„ฑ ์กฐ์ ˆ
  • ํˆฌ์ž ์ „๋žต 4. ๋“€์–ผ ๋ชจ๋ฉ˜ํ…€ + ํ˜„๊ธˆ ๋น„์ค‘ ์ตœ์†Œ 90%
  • ํˆฌ์ž ์ „๋žต 5. ์˜ค์ „ ์ฒœ๊ตญ, ์˜คํ›„ ์ง€์˜ฅ
  • ์ค‘๊ฐ„ ์ •๋ฆฌ

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  1. 2021 TIL
  2. MAY

1 Sat

๊ฐ€์ƒํ™”ํ ํˆฌ์ž๊ณต์‹

PART4. ๊ฐ€์ƒํ™”ํ ์‹ค์ „ ํˆฌ์ž ์ „๋žต -1

  • ํ•ด๋‹น ๊ธฐ๋ก์€ 2014๋…„ 2์›”๋ถ€ํ„ฐ 2018๋…„ 3์›”๊นŒ์ง€์˜ ๊ฒฐ๊ณผ๋ฅผ ๋ฐฑํ…Œ์ŠคํŒ…ํ•œ ๊ฒฐ๊ณผ์˜ ๋ถˆ๊ณผํ•˜๋ฉฐ ์–ธ์ œ๋“ ์ง€ ํฐ ํญ์˜ ํ•˜๋ฝ์žฅ์ด ์˜ฌ ์ˆ˜ ์žˆ๋‹ค.

    • ๊ฑฐ๋ž˜ ๋น„์šฉ(์ˆ˜์ˆ˜๋ฃŒ + ์Šฌ๋ฆฌํ”ผ์ง€) 0.2% ์ ์šฉ

  • ๋”ฐ๋ผ์„œ, ์ด ์ „๋žต์—์„œ ์ œ์‹œํ•˜๋Š” ์ˆ˜์ต์— ํ˜นํ•˜์ง€ ๋ง ๊ฒƒ์ด๋ฉฐ ์ตœ๋Œ€ํ•œ MDD๋ฅผ ์ž˜ ๊ด€๋ฆฌํ•˜๋ผ.

  • ์˜ค๋Š˜ 50%๋ฅผ ๋”ฐ๋Š” ์‚ฌ๋žŒ์€ ์–ด๋А ๋‚  80%๋ฅผ ์žƒ์„ ์ˆ˜ ์žˆ๋‹ค. ๋ฏธ๋ž˜์—๋Š” ์˜ˆ์ƒํ•œ MDD๋ณด๋‹ค 2๋ฐฐ ์ •๋„ ๋†’์•„์งˆ ์ˆ˜ ์žˆ๋‹ค๊ณ  ๊ฐ์˜คํ•  ๊ฒƒ.

ํˆฌ์ž ์ „๋žต 1. ๊ฐ€์ƒํ™”ํ 10% + ํ˜„๊ธˆ 90%

  • ๋ฌด์‹ํ•˜๊ณ  ๋‹จ์ˆœํ•œ ์ „๋žต. ํˆฌ์ž์— ๋งŽ์€ ์‹œ๊ฐ„์„ ์•ˆ์“ฐ๊ธฐ ์œ„ํ•จ.

  • ๋ฐฉ๋ฒ•

    • ์ž์‚ฐ์˜ 10%๋งŒ ๊ฐ€์ƒํ™”ํ์— ํˆฌ์žํ•œ๋‹ค.

    • ๊ฐ€์ƒํ™”ํ ์ž์‚ฐ์ด N% ์ƒ์Šนํ•˜๋ฉด ์ผ๋ถ€๋ฅผ ํŒ”์•„์„œ ๋‹ค์‹œ ํ˜„๊ธˆ 90%, ๊ฐ€์ƒํ™”ํ 10%๋กœ ๋น„์ค‘์„ ๋งž์ถ˜๋‹ค.

    • ๊ฐ€์ƒํ™”ํ ์ž์‚ฐ์ด M% ํ•˜๋ฝํ•˜๋ฉด ์ผ๋ถ€๋ฅผ ์ถ”๊ฐ€๋งค์ˆ˜ํ•ด์„œ ํ˜„๊ธˆ 90%, ๊ฐ€์ƒํ™”ํ 10%๋กœ ๋น„์ค‘์„ ๋งž์ถ˜๋‹ค.

  • ๊ฒฐ๊ณผ

    • ๋ณต๋ฆฌ์ˆ˜์ต 29.7%

    • MDD 10.5%

    • ํ•˜๋ฃจ ์•ˆ์— ์ž์‚ฐ 1%๊ฐ€ ๊นจ์ง„ ๊ฒฝ์šฐ๋Š” 26๋ฒˆ(1.7%)

    • ํ•˜๋ฃจ ์•ˆ์— ์ž์‚ฐ 2%๊ฐ€ ๊นจ์ง„ ๊ฒฝ์šฐ๋Š” 4๋ฒˆ(0.3%)

ํˆฌ์ž ์ „๋žต 2. ์ด๋™ํ‰๊ท  + ํ˜„๊ธˆ ๋น„์ค‘ 80%์ด์ƒ

  • ๋ฐฉ๋ฒ•

    • ์„ ํƒํ•œ ๊ฐ€์ƒํ™”ํ์˜ 5์ผ ์ด๋™ํ‰๊ท ์„ 1์ผ 1ํšŒ ์ฒดํฌ

    • ํ˜„์žฌ๊ฐ€๊ฒฉ์ด ์ด๋™ํ‰๊ท ๋ณด๋‹ค ๋†’์œผ๋ฉด ๋งค์ˆ˜ ๋˜๋Š” ๋ณด์œ 

    • ํ˜„์žฌ ๊ฐ€๊ฒฉ์ด ์ด๋™ํ‰๊ท ๋ณด๋‹ค ๋‚ฎ์œผ๋ฉด ๋งค๋„ ๋˜๋Š” ๋ณด๋ฅ˜

    • ํ˜„๊ธˆ ๋น„์ค‘์€ 80% ์œ ์ง€

    • ์ž๊ธˆ ๊ด€๋ฆฌ : ํˆฌ์ž… ๊ธˆ์•ก์€ 20% / ๊ฐ€์ƒํ™”ํ ์ˆ˜

      • ์„ ํƒํ•œ ๊ฐ€์ƒํ™”ํ๊ฐ€ 4๊ฐœ๋ฉด ๊ฐ๊ฐ 5%์”ฉ ํˆฌ์ž

  • ํ˜„๊ธˆ ๋น„์ค‘์„ 80%๋กœ ์œ ์ง€ํ•œ ๊ฒฐ๊ณผ

    • ๋ณต๋ฆฌ์ˆ˜์ต 36.1%

    • MDD 8.5%

    • ํ•˜๋ฃจ ์•ˆ์— ์ž์‚ฐ 1%๊ฐ€ ๊นจ์ง„ ๊ฒฝ์šฐ๋Š” 33๋ฒˆ(2.2%)

      ํ•˜๋ฃจ ์•ˆ์— ์ž์‚ฐ 2%๊ฐ€ ๊นจ์ง„ ๊ฒฝ์šฐ๋Š” 6๋ฒˆ(0.4%)

  • ํ˜„๊ธˆ ๋น„์ค‘์„ 90%๋กœ ์œ ์ง€ํ•œ ๊ฒฐ๊ณผ

    • ๋ณต๋ฆฌ์ˆ˜์ต 16.9%

    • MDD 4.3%

    • ํ•˜๋ฃจ ์•ˆ์— ์ž์‚ฐ 1%๊ฐ€ ๊นจ์ง„ ๊ฒฝ์šฐ๋Š” 6๋ฒˆ(0.4%)

    • ํ•˜๋ฃจ ์•ˆ์— ์ž์‚ฐ 2%๊ฐ€ ๊นจ์ง„ ๊ฒฝ์šฐ๋Š” 0๋ฒˆ(0.0%)

ํˆฌ์ž ์ „๋žต 3. ์Šˆํผ ์ƒ์Šน์žฅ + ๋ณ€๋™์„ฑ ์กฐ์ ˆ

  • ๋ณ€๋™์„ฑ์„ ๋งž์ถ”๋ฉด์„œ ์ƒ์Šน์žฅ์— ํˆฌ์žํ•˜๋Š” ๋ฐฉ๋ฒ•.

    • ํ˜„๊ธˆ ์ž์‚ฐ์„ 90%, 80%๋กœ ๊ณ ์ •ํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ์œ ๋™์ ์œผ๋กœ ํˆฌ์žํ•œ๋‹ค.

    • ๋ณ€๋™์„ฑ์ด ํฌ๋ฉด ๋‚ฎ์€ ๊ธˆ์•ก์„, ๋‚ฎ์œผ๋ฉด ํฐ ๊ธˆ์•ก์„ ํˆฌ์žํ•˜๋Š” ๊ฒƒ์ด ์œ ๋ฆฌํ•˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค.

  • ๋ฐฉ๋ฒ•

    • ๋ณ€๋™์„ฑ N%๋ฅผ ์„ค์ •ํ•œ๋‹ค.

    • ๊ฐ ์ฝ”์ธ์˜ ๋ณ€๋™์„ฑ a%, b%, c%๋ฅผ ์กฐ์‚ฌํ•œ๋‹ค. (์˜ˆ์‹œ์—์„œ๋Š” 3๊ฐœ)

    • ๊ฐ ์ฝ”์ธ์ด 3, 5, 10, 20์ผ ์ด๋™ํ‰๊ท ์„ ๋ณด๋‹ค ๋†’์€์ง€ ์กฐ์‚ฌํ•œ ๋’ค, ๋†’๋‹ค๋ฉด N/a, N/b, N/c ๋งŒํผ ํˆฌ์žํ•œ๋‹ค.

  • ๊ฒฐ๊ณผ

    • ๋ณต๋ฆฌ์ˆ˜์ต 50.8%

    • MDD 6.73%

    • ์Šน๋ฅ  52.97%

    • ์†์ต๋น„ 1.73

    • ํ•˜๋ฃจ ์•ˆ์— ์ž์‚ฐ 1%๊ฐ€ ๊นจ์ง„ ๊ฒฝ์šฐ๋Š” 46๋ฒˆ(3.1%)

    • ํ•˜๋ฃจ ์•ˆ์— ์ž์‚ฐ 2%๊ฐ€ ๊นจ์ง„ ๊ฒฝ์šฐ๋Š” 4๋ฒˆ(0.3%)

ํˆฌ์ž ์ „๋žต 4. ๋“€์–ผ ๋ชจ๋ฉ˜ํ…€ + ํ˜„๊ธˆ ๋น„์ค‘ ์ตœ์†Œ 90%

  • ์š”์ฆ˜ ํ•ซํ•œ ์ฝ”์ธ์— ์˜ฌ๋ผํƒ€๋Š”, ํŠธ๋ Œ๋“œ์— ํˆฌ์žํ•˜๋Š” ์ „๋žต

  • ๋ฐฉ๋ฒ•

    • ์„ ํƒํ•œ ๊ฐ€์ƒํ™”ํ์˜ ๊ณผ๊ฑฐ 30์ผ ์ˆ˜์ต๋ฅ  ์ฒดํฌ

    • ์ตœ๊ทผ 30์ผ๊ฐ„ ๊ฐ€์žฅ ์ˆ˜์ต๋ฅ ์ด ์ข‹์€ ๊ฐ€์ƒํ™”ํ์— ์ž์‚ฐ 10% ํˆฌ์ž…

    • ์ตœ๊ทผ 30์ผ๊ฐ„ ๊ฐ€์žฅ ์ˆ˜์ต๋ฅ ์ด ์ข‹์€ ๊ฐ€์ƒํ™”ํ๊ฐ€ ๋ฐ”๋€” ๊ฒฝ์šฐ ๊ธฐ์กด์— ๋ณด์œ ํ•œ ํ™”ํ๋Š” ๋งค๋„ํ•˜๋ฉฐ, ์ƒˆ๋กœ์šด ํ™”ํ๋ฅผ ๋งค์ˆ˜ํ•œ๋‹ค.

    • ์„ ํƒํ•œ ๋ชจ๋“  ๊ฐ€์ƒํ™”ํ์˜ 30์ผ ์ˆ˜์ต๋ฅ ์ด ๋งˆ์ด๋„ˆ์Šค๋กœ ๋Œ์•„์„ค ๊ฒฝ์šฐ ๋ชจ๋“  ๊ฐ€์ƒํ™”ํ ๋งค๋„ํ•˜๋ฉฐ ํ˜„๊ธˆ์œผ๋กœ ๋ณด์œ ํ•œ๋‹ค.

  • ๊ฒฐ๊ณผ

    • ๋ณต๋ฆฌ์ˆ˜์ต 49.92%

    • MDD 10.4%

    • ํ•˜๋ฃจ ์•ˆ์— ์ž์‚ฐ 1%๊ฐ€ ๊นจ์ง„ ๊ฒฝ์šฐ๋Š” 53๋ฒˆ(3.6%)

    • ํ•˜๋ฃจ ์•ˆ์— ์ž์‚ฐ 2%๊ฐ€ ๊นจ์ง„ ๊ฒฝ์šฐ๋Š” 11๋ฒˆ(0.8%)

ํˆฌ์ž ์ „๋žต 5. ์˜ค์ „ ์ฒœ๊ตญ, ์˜คํ›„ ์ง€์˜ฅ

  • ๋น„ํŠธ์ฝ”์ธ์€ ์˜ค์ „ ์ˆ˜์ต์ด ์˜คํ›„๋ณด๋‹ค ์••๋„์ ์œผ๋กœ ๋†’๋‹ค.

  • ์ด๋”๋ฆฌ์›€(17.03~18.03)

    • ์˜ค์ „ ์ˆ˜์ต : 3,631.4 %

    • ์˜คํ›„ ์ˆ˜์ต : -86.7%

    • ๊ฑฐ๋ž˜ ๋น„์šฉ : 0.1%

  • ๋ฆฌํ”Œ(17.10~18.03)

    • ์˜ค์ „ ์ˆ˜์ต : 44.9%

    • ์˜คํ›„ ์ˆ˜์ต : 8.8%

    • ๊ฑฐ๋ž˜ ๋น„์šฉ : 0.1%

  • ์ž์ •์— ๋งค์ˆ˜ํ•˜๊ณ  ์ •์˜ค์— ๋งค๋„ํ•œ๋‹ค.

  • ์ด ๋•Œ, ์ „๋‚  ์˜คํ›„(12~24) ์ˆ˜์ต์ด 0 ์ด์ƒ์ผ ๋•Œ๋งŒ ์˜ค์ „ ํˆฌ์ž๋ฅผ ํ•œ๋‹ค.

  • ์ง€์ •๊ฐ€ ๋งค์ˆ˜๊ฐ€ ์–‘ํ˜ธํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ฑฐ๋ž˜ ๋น„์šฉ๋„ ์ƒ๋Œ€์ ์œผ๋กœ ์ ๊ฒŒ ๋ฐœ์ƒํ•œ๋‹ค.

  • ๋ฐฉ๋ฒ•

    • ์˜ค์ „ 0์‹œ์— ๊ฐ€์ƒํ™”ํ์˜ ์ „์ผ ์˜คํ›„(12~24์‹œ) ์ˆ˜์ต๋ฅ ๊ณผ ๊ฑฐ๋ž˜๋Ÿ‰ ์ฒดํฌ

    • ๋งค์ˆ˜ : ์ „์ผ ์˜คํ›„ ์ˆ˜์ต๋ฅ  >0 & ์ „์ผ ์˜คํ›„ ๊ฑฐ๋ž˜๋Ÿ‰ > ์ „์ผ ์˜ค์ „ ๊ฑฐ๋ž˜๋Ÿ‰

    • ๋งค๋„ : ์ •์˜ค

    • ์ž๊ธˆ ๊ด€๋ฆฌ : ๊ฐ€์ƒํ™”ํ๋ณ„ ํˆฌ์ž… ๊ธˆ์•ก = ๋ชฉํ‘œ ๋ณ€๋™์„ฑ / ํ™”ํ์˜ ์ „์ผ ์˜คํ›„ ๋ณ€๋™์„ฑ / ํˆฌ์ž ๋Œ€์ƒ ํ™”ํ์ˆ˜

  • ๋‹จ์ˆœ ์˜ค์ „ ๋งค์ˆ˜

    • ๋ณต๋ฆฌ์ˆ˜์ต 61.13%

    • MDD 70.02%

    • ์Šน๋ฅ  48.3%

    • ์†์ต๋น„ 1.33

  • ์ „์ผ ์˜คํ›„ ์ˆ˜์ต๋ฅ  ์ƒ์Šน์‹œ ์˜ค์ „ ๋งค์ˆ˜

    • ๋ณต๋ฆฌ์ˆ˜์ต 220.34%

    • MDD 25.27%

    • ์Šน๋ฅ  55.1%

    • ์†์ต๋น„ 1.82

  • ์ „์ผ ์˜คํ›„ ์ˆ˜์ต๋ฅ  ๋ฐ ๊ฑฐ๋ž˜๋Ÿ‰ ์ƒ์Šน ์‹œ ์˜ค์ „ ๋งค์ˆ˜

    • ๋ณต๋ฆฌ์ˆ˜์ต 218.82%

    • MDD 17.98%

    • ์Šน๋ฅ  58.1%

    • ์†์ต๋น„ 2.04

  • ๋ณ€๋™์„ฑ 2% + ์ „์ผ ์˜คํ›„ ์ˆ˜์ต๋ฅ  ๋ฐ ๊ฑฐ๋ž˜๋Ÿ‰ ์ƒ์Šน ์‹œ ์˜ค์ „ ๋งค์ˆ˜

    • ๋ณต๋ฆฌ์ˆ˜์ต 80.6%

    • MDD 10.5%

    • ์Šน๋ฅ  58.2%

    • ์†์ต๋น„ 2.15

  • ๋ณ€๋™์„ฑ 1% + ์ „์ผ ์˜คํ›„ ์ˆ˜์ต๋ฅ  ๋ฐ ๊ฑฐ๋ž˜๋Ÿ‰ ์ƒ์Šน ์‹œ ์˜ค์ „ ๋งค์ˆ˜

    • ๋ณต๋ฆฌ์ˆ˜์ต 39.47%

    • MDD 5.78%

  • ๋ถ„์„

    • ๋‹จ์ˆœ ์˜ค์ „ ๋งค์ˆ˜์™€ ์ „์ผ ์˜คํ›„ ์ˆ˜์ต๋ฅ ์ด ์ƒ์Šนํ•  ๊ฒฝ์šฐ ์˜ค์ „ ๋งค์ˆ˜๋Š” ์‹ค์ ์ด ํ•˜๋Š˜๊ณผ ๋•… ์ฐจ์ด๋‹ค.

    • ์ด ์ˆ˜์ต์ด 20๋ฐฐ(๋ณต๋ฆฌ์ˆ˜์ต์€ ์•ฝ 4๋ฐฐ) ๋†’์•„์กŒ๊ณ  MDD๋Š” 1/3๋กœ ๊ธ‰๊ฐํ–ˆ๋‹ค. ์†์ต๋น„์™€ ์Šน๋ฅ ๋„ ๋ˆˆ์— ๋„๊ฒŒ ๋†’์•„์กŒ๋‹ค.

    • ๋˜ํ•œ, ๊ฑฐ๋ž˜๋Ÿ‰ ์ƒ์Šน์‹œ์—๋งŒ ์˜ค์ „ ๋งค์ˆ˜ํ–ˆ์„ ๊ฒฝ์šฐ ์ˆ˜์ต๋ฅ ์€ ๋น„์Šทํ•œ๋ฐ MDD๊ฐ€ ๋งŽ์ด ์ค„์—ˆ๊ณ  ์Šน๋ฅ ๊ณผ ์†์ต๋น„๋„ ๊ฐœ์„ ๋˜์—ˆ๋‹ค.

    • ๊ทธ๋Ÿฌ๋‚˜ MDD 18%๋Š” ๋‚ฎ์ง€ ์•Š์€ ์ˆ˜์น˜์ธ๋ฐ, ์—ฌ๊ธฐ์— ๋ชฉํ‘œ ๋ณ€๋™์„ฑ์„ ์„ค์ •ํ•˜์—ฌ MDD๋ฅผ ์กฐ๊ธˆ ๋” ๋‚ฎ์ถœ ์ˆ˜์žˆ๋‹ค.

      • ๋ชฉํ‘œ ๋ณ€๋™์„ฑ์€ ์ž์‹ ์˜ ์ž์‚ฐ์ด ์ด๋งŒํผ์˜ ๋ณ€๋™๋ฅ ๋งŒ์„ ๊ฐ€์ง€๊ธฐ๋ฅผ ์›ํ•œ๋‹ค๋Š” ๋œป์ด๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด 2%์˜ ๋ชฉํ‘œ ๋ณ€๋™์„ฑ์„ ์„ค์ •ํ•˜๋ฉด ์ž์‹ ์˜ ์ž์‚ฐ์€ 2%์˜ ๋ฒ”์œ„ ์•ˆ์—์„œ ์ˆ˜์ต ํ˜น์€ ์†ํ•ด๋ฅผ ๋ณด๊ฒŒ ๋œ๋‹ค.

      • ๋‹จ, ์ด๋Š” ๋ณ€๋™์„ฑ์€ ๊ณผ๊ฑฐ์˜ ๋ฐ์ดํ„ฐ๋กœ ๋งŒ๋“  ์ง€ํ‘œ์— ๋ถˆ๊ณผํ•˜๋ฉฐ 2%๋กœ ์„ค์ •ํ•˜๋”๋ผ๋„ ํ˜„์žฌ์˜ ๊ฐ€๊ฒฉ์ด ๊ต‰์žฅํžˆ ํฌ๊ฒŒ ๋ฐ”๋€Œ๋ฉด 2%์˜ ๋ฒ”์œ„ ๋ฐ–์œผ๋กœ๋„ ์ˆ˜์ง€๊ฐ€ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค.

์ค‘๊ฐ„ ์ •๋ฆฌ

  • ์ „๋žต 1. ๊ฐ€์ƒํ™”ํ 10% + ํ˜„๊ธˆ 90%

    • ๋ณต๋ฆฌ์ˆ˜์ต 29.7% MDD 10.5%

  • ์ „๋žต 2. ์ด๋™ํ‰๊ท  + ํ˜„๊ธˆ ๋น„์ค‘ 80%์ด์ƒ

    • ํ˜„๊ธˆ ๋น„์ค‘ 80% ๋ณต๋ฆฌ์ˆ˜์ต 36.1% MDD 8.5%

    • ํ˜„๊ธˆ ๋น„์ค‘ 90% ๋ณต๋ฆฌ์ˆ˜์ต 16.9% MDD 4.3%

  • ์ „๋žต 3. ์Šˆํผ ์ƒ์Šน์žฅ + ๋ณ€๋™์„ฑ ์กฐ์ ˆ

    • ๋ณ€๋™์„ฑ 2% ๋ณต๋ฆฌ์ˆ˜์ต 50.8% MDD 6.73%

  • ์ „๋žต 4. ๋“€์–ผ ๋ชจ๋ฉ˜ํ…€ + ํ˜„๊ธˆ ๋น„์ค‘ ์ตœ์†Œ 90%

    • ๋ณต๋ฆฌ์ˆ˜์ต 49.92% MDD 10.4%

  • ์ „๋žต 5. ์˜ค์ „ ์ฒœ๊ตญ, ์˜คํ›„ ์ง€์˜ฅ

    • ๋‹จ์ˆœ ์˜ค์ „ ๋งค์ˆ˜ ๋ณต๋ฆฌ์ˆ˜์ต 61.13% MDD 70.02%

    • ์ „์ผ ์˜คํ›„ ์ˆ˜์ต๋ฅ  ์ƒ์Šน์‹œ ์˜ค์ „ ๋งค์ˆ˜ ๋ณต๋ฆฌ์ˆ˜์ต 220.34% MDD 25.27%

    • ์ „์ผ ์˜คํ›„ ์ˆ˜์ต๋ฅ  ๋ฐ ๊ฑฐ๋ž˜๋Ÿ‰ ์ƒ์Šน ์‹œ ์˜ค์ „ ๋งค์ˆ˜ ๋ณต๋ฆฌ์ˆ˜์ต 218.82% MDD 17.98%

    • ๋ณ€๋™์„ฑ 2% + ์ „์ผ ์˜คํ›„ ์ˆ˜์ต๋ฅ  ๋ฐ ๊ฑฐ๋ž˜๋Ÿ‰ ์ƒ์Šน ์‹œ ์˜ค์ „ ๋งค์ˆ˜ ๋ณต๋ฆฌ์ˆ˜์ต 80.6% MDD 10.5%

    • ๋ณ€๋™์„ฑ 1% + ์ „์ผ ์˜คํ›„ ์ˆ˜์ต๋ฅ  ๋ฐ ๊ฑฐ๋ž˜๋Ÿ‰ ์ƒ์Šน ์‹œ ์˜ค์ „ ๋งค์ˆ˜ ๋ณต๋ฆฌ์ˆ˜์ต 39.47% MDD 5.78%

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