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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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  • ๋ถ€๋ถ„๊ณต๊ฐ„๊ณผ ๋ถ€๋ถ„๊ณต๊ฐ„์˜ ๊ธฐ์ €
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  • ์˜๊ณต๊ฐ„๊ณผ ์—ด๊ณต๊ฐ„
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  • ๊ณ ์œ ๊ฐ’, ๊ณ ์œ ๋ฒกํ„ฐ, ๊ณ ์œ ๊ณต๊ฐ„

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  1. TIL : Python & Math

Linear Algebra(Khan Academy)

Previous๋‹จ ๋‘ ์žฅ์˜ ๋ฌธ์„œ๋กœ ๋ฐ์ดํ„ฐ ๋ถ„์„๊ณผ ์‹œ๊ฐํ™” ๋ฝ€๊ฐœ๊ธฐNext์ธ๊ณต์ง€๋Šฅ์„ ์œ„ํ•œ ์„ ํ˜•๋Œ€์ˆ˜

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๋ฒกํ„ฐ์™€ ๊ณต๊ฐ„

๋ฒกํ„ฐ

์„ ํ˜•๊ฒฐํ•ฉ๊ณผ ์ƒ์„ฑ

์„ ํ˜•์ข…์†๊ณผ ๋…๋ฆฝ

๋ถ€๋ถ„๊ณต๊ฐ„๊ณผ ๋ถ€๋ถ„๊ณต๊ฐ„์˜ ๊ธฐ์ €

๋ฒกํ„ฐ์˜ ๋‚ด์ ๊ณผ ์™ธ์ 

๋ฒกํ„ฐ์˜ ์™ธ์ ์ด๋ž€?

์ฆ๋ช… : ์™ธ์ ๊ณผ ๊ฐ์˜ ์‚ฌ์ธ๊ฐ’๊ณผ์˜ ๊ด€๊ณ„

๋‚ด์ ๊ณผ ์™ธ์ ์˜ ๋น„๊ต/์ง๊ด€

๋ฒกํ„ฐ์˜ ์‚ผ์ค‘์ ์˜ ํ™•์žฅ

ํ‰๋ฉด๋ฐฉ์ •์‹์˜ ๋ฒ•์„  ๋ฒกํ„ฐ

์ ๊ณผ ํ‰๋ฉด ์‚ฌ์ด์˜ ๊ฑฐ๋ฆฌ

ํ‰๋ฉด ์‚ฌ์ด์˜ ๊ฑฐ๋ฆฌ

๊ฐ€๊ฐ๋ฒ•์œผ๋กœ ์—ฐ๋ฆฝ๋ฐฉ์ •์‹์„ ํ’€๊ธฐ ์œ„ํ•œ ํ–‰๋ ฌ

ํ–‰ ์‚ฌ๋‹ค๋ฆฌ๊ผด ํ–‰๋ ฌ์„ ์ด์šฉํ•˜์—ฌ 3์ฐจ์—ฐ๋ฆฝ๋ฐฉ์ •์‹๊ณผ 4๊ฐœ์˜ ๋ณ€์ˆ˜ ํ’€๊ธฐ

ํ–‰๋ ฌ์„ ์ด์šฉํ•˜์—ฌ ์„ ํ˜•๊ณ„ ํ’€๊ธฐ

ํ–‰ ์‚ฌ๋‹ค๋ฆฌ๊ผด์„ ์ด์šฉํ•˜์—ฌ ์„ ํ˜•๊ณ„๋Š” ํ•ด๊ฐ€ ์—†๋‹ค๋Š” ๊ฒƒ์„ ์•Œ์•„๋ณด๊ธฐ

์˜๊ณต๊ฐ„๊ณผ ์—ด๊ณต๊ฐ„

ํ–‰๋ ฌ๋ณ€ํ™˜

ํ•จ์ˆ˜์™€ ์„ ํ˜•๋ณ€ํ™˜

์„ ํ˜•๋ณ€ํ™˜ ์˜ˆ์ œ

๋ณ€ํ™˜๊ณผ ํ–‰๋ ฌ์˜ ๊ณฑ

์—ญํ•จ์ˆ˜์™€ ์—ญ๋ณ€ํ™˜

์—ญ์ˆ˜์™€ ํ–‰๋ ฌ์‹ ๊ตฌํ•˜๊ธฐ

์—ญ๋ณ€ํ™˜ ํŒ๋ณ„ํ•˜๋Š” ๋ฐฉ๋ฒ• ์ถ”๋ก ํ•˜๊ธฐ

์—ญํ–‰๋ ฌ ๊ตฌํ•˜๊ธฐ ์˜ˆ์ œ

2x2 ์—ญํ–‰๋ ฌ์˜ ์‹

3 x 3 ํ–‰๋ ฌ์‹

n x n ํ–‰๋ ฌ์‹

๋‹ค๋ฅธ ํ–‰/์—ด์„ ์ด์šฉํ•˜์—ฌ ํ–‰๋ ฌ์‹ ๊ตฌํ•˜๊ธฐ

์‚ฌ๋ฃจ์Šค ๋ฒ•์น™์˜ ํ–‰๋ ฌ์‹

ํ–‰๋ ฌ์‹ ์‹ฌํ™”

ํ–‰์„ ์Šค์นผ๋ผ๋กœ ๊ณฑํ–ˆ์„ ๋•Œ์˜ ํ–‰๋ ฌ์‹

ํ–‰์˜ ์Šค์นผ๋ผ ๊ณฑ

ํ–‰์ด ๋”ํ•ด์กŒ์„ ๋•Œ์˜ ํ–‰๋ ฌ์‹

๋ณต์ œ๋œ ํ–‰์˜ ํ–‰๋ ฌ์‹

ํ–‰์—ฐ์‚ฐ ํ›„์˜ ํ–‰๋ ฌ์‹

์ƒ์‚ผ๊ฐํ–‰๋ ฌ์‹

๊ฐ„๋‹จํ•œ 4x4 ํ–‰๋ ฌ์‹

ํ–‰๋ ฌ์‹๊ณผ ํ‰ํ–‰์‚ฌ๋ณ€ํ˜•์˜ ์˜์—ญ

์Šค์ผ€์ผ ๋ณ€ํ™˜์ธ์ž๋กœ์จ์˜ ํ–‰๋ ฌ์‹

์ „์น˜ ํ–‰๋ ฌ

์ „์น˜ํ–‰๋ ฌ

์ „์น˜ํ–‰๋ ฌ์˜ ํ–‰๋ ฌ์‹

ํ–‰๋ ฌ๊ณฑ์˜ ์ „์น˜ํ–‰๋ ฌ

์ „์น˜ํ–‰๋ ฌ์˜ ํ•ฉ๊ณผ ์—ญ

์ „์น˜๋ฒกํ„ฐ

ํ–‰๊ณต๊ฐ„๊ณผ ์ขŒ์˜๊ณต๊ฐ„

์ขŒ์˜๊ณต๊ฐ„๊ณผ ํ–‰๊ณต๊ฐ„์˜ ์‹œ๊ฐํ™”

rank(A) = rank(A์˜ ์ „์น˜ํ–‰๋ ฌ)

A์˜ ์ „์น˜ํ–‰๋ ฌ x A ์˜ ๊ฐ€์—ญ์„ฑ ํ™•์ธํ•˜๊ธฐ

์ƒํ˜ธ ์ขŒํ‘œ๊ณ„(๊ธฐ์ €)

์ง๊ต๋ณด ๊ณต๊ฐ„

์ง๊ต๋ณด๊ณต๊ฐ„

dim(v) + dim(v์˜ ์ง๊ต๋ณด๊ณต๊ฐ„) = n

๋ถ€๋ถ„๊ณต๊ฐ„์˜ ์›์†Œ๋ฅผ ์ด์šฉํ•˜์—ฌ Rn์˜ ๋ฒกํ„ฐ๋ฅผ ๋‚˜ํƒ€๋‚ด๊ธฐ

์ง๊ต๋ณด๊ณต๊ฐ„์˜ ์ง๊ต๋ณด๊ณต๊ฐ„

์˜๊ณต๊ฐ„์˜ ์ง๊ต๋ณด๊ณต๊ฐ„

Ax = b ์˜ ์œ ์ผํ•œ ํ–‰๊ณต๊ฐ„์˜ ํ•ด

Ax=b์˜ ํ–‰๊ณต๊ฐ„ ํ•ด ์˜ˆ์ œ

์ •์‚ฌ์˜

๋ถ€๋ถ„๊ณต๊ฐ„์— ๋Œ€ํ•œ ์ •์‚ฌ์˜

ํ‰๋ฉด์— ๋Œ€ํ•œ ์ •์‚ฌ์˜ ์‹œ๊ฐํ™”

๋ถ€๋ถ„๊ณต๊ฐ„์— ๋Œ€ํ•œ ์ •์‚ฌ์˜์ด ์„ ํ˜•๋ณ€ํ™˜์ด๋ผ๋Š” ๊ฒƒ ํ™•์ธํ•˜๊ธฐ

๋ถ€๋ถ„๊ณต๊ฐ„ ์ •์‚ฌ์˜ ํ–‰๋ ฌ์˜ ์˜ˆ์ œ

์ •์‚ฌ์˜ ํ–‰๋ ฌ์˜ ๋‹ค๋ฅธ ์˜ˆ์ œ

์ •์‚ฌ์˜์€ ๋ถ€๋ถ„๊ณต๊ฐ„์—์„œ ์ œ์ผ ๊ฐ€๊นŒ์šด ๋ฒกํ„ฐ๋ผ๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜๊ธฐ

์ตœ์†Œ์ œ๊ณฑ ๊ทผ์‚ฟ๊ฐ’

์ตœ์†Œ์ œ๊ณฑ ์˜ˆ์ œ

๋‹ค๋ฅธ ์ตœ์†Œ์ œ๊ณฑ์˜ ์˜ˆ์ œ

๊ธฐ์ €๋ณ€ํ™˜

๊ธฐ์ €์— ๋Œ€ํ•œ ์ขŒํ‘œ

๊ธฐ์ €๋ณ€ํ™˜ํ–‰๋ ฌ

๊ฐ€์—ญ์„ฑ์ด ์žˆ๋Š” ๊ธฐ์ €๋ณ€ํ™˜ํ–‰๋ ฌ

๊ธฐ์ €์— ๋Œ€ํ•œ ๋ณ€ํ™˜ํ–‰๋ ฌ

๋ฒˆ๊ฐˆ์•„ ์ƒ๊ธฐ๋Š” ๊ธฐ์ €์˜ ๋ณ€ํ™˜ํ–‰๋ ฌ ์˜ˆ์ œ

๋ฒˆ๊ฐˆ์•„ ์ƒ๊ธฐ๋Š” ๊ธฐ์ €์˜ ๋ณ€ํ™˜ํ–‰๋ ฌ ์˜ˆ์ œ 2

์ขŒํ‘œ๊ณ„ ๋ณ€ํ™˜์„ ์ด์šฉํ•œ ๋ณ€ํ™˜ํ–‰๋ ฌ ๊ตฌํ•˜๊ธฐ

์ •๊ทœ์ง๊ต์ง€์ €์™€ ๊ทธ๋žŒ-์Šˆ๋ฏธํŠธ ๊ณผ์ •

์ •๊ทœ์ง๊ต๊ธฐ์ €๋ž€?

์ •๊ทœ์ง๊ต๊ธฐ์ €์— ๋Œ€ํ•œ ์ขŒํ‘œ

์ •๊ทœ์ง๊ต๊ธฐ์ €๋ฅผ ์ด์šฉํ•œ ๋ถ€๋ถ„๊ณต๊ฐ„์— ๋Œ€ํ•œ ์ •์‚ฌ์˜

์ •๊ทœ์ง๊ต๊ธฐ์ €๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ถ€๋ถ„๊ณต๊ฐ„์— ๋Œ€ํ•œ ์ •์‚ฌ์˜์„ ์ฐพ๋Š” ๋ฐฉ๋ฒ•์˜ ์˜ˆ์ œ

๋ณ€ํ™˜ํ–‰๋ ฌ์„ ์ฐพ๊ธฐ ์œ„ํ•œ ์ง๊ต๊ธฐ์ €๋ณ€ํ™˜ํ–‰๋ ฌ์„ ์ด์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์˜ ์˜ˆ์ œ

๊ฐ๋„์™€ ๊ธธ์ด๋ฅผ ๋ณด์กดํ•˜๋Š” ์ง๊ตํ–‰๋ ฌ

๊ทธ๋žŒ-์Šˆ๋ฏธํŠธ ๊ณผ

๊ทธ๋žŒ-์Šˆ๋ฏธํŠธ ๊ณผ์ • ์˜ˆ์ œ

3๊ฐœ์˜ ๊ธฐ์ €๋ฒกํ„ฐ๋ฅผ ์ด์šฉํ•œ ๊ทธ๋žŒ-์Šˆ๋ฏธํŠธ ๊ณผ์ • ์˜ˆ์ œ

๊ณ ์œ ๊ฐ’, ๊ณ ์œ ๋ฒกํ„ฐ, ๊ณ ์œ ๊ณต๊ฐ„

๊ณ ์œ ๊ฐ’๊ณผ ๊ณ ์œ ๋ฒกํ„ฐ๋ž€?

๊ณ ์œ ๊ฐ’์„ ๊ฒฐ์ •ํ•˜๋Š” ์‹ ์ฆ๋ช…

2x2 ํ–‰๋ ฌ์˜ ๊ณ ์œ ๊ฐ’์„ ๊ตฌํ•˜๋Š” ์˜ˆ์ œ

๊ณ ์œ ๋ฒกํ„ฐ์™€ ๊ณ ์œ ๊ณต๊ฐ„์„ ๊ตฌํ•˜๋Š” ์˜ˆ์ œ

3x3 ํ–‰๋ ฌ์˜ ๊ณ ์œ ๊ฐ’

3x3 ํ–‰๋ ฌ์˜ ๊ณ ์œ ๋ฒกํ„ฐ์™€ ๊ณ ์œ ๊ณต๊ฐ„

๊ณ ์œ ๊ธฐ์ €์™€ ์ขŒํ‘œ๊ณ„

์„ ํ˜•๋Œ€์ˆ˜ํ•™์„ ์œ„ํ•œ ๋ฒกํ„ฐ๋ž€?
์‹ค์ขŒํ‘œ๊ณต๊ฐ„
๋Œ€์ˆ˜์™€ ๊ทธ๋ž˜ํ”„๋ฅผ ์ด์šฉํ•œ ๋ฒกํ„ฐ์˜ ๋ง์…ˆ
๋ฒกํ„ฐ์™€ ์Šค์นผ๋ผ์˜ ๊ณฑ์…ˆ
๋ฒกํ„ฐ ์˜ˆ์ œ
๋‹จ์œ„๋ฒกํ„ฐ๋ž€?
์ง์„ ์˜ ๋งค๊ฐœ๋ณ€์ˆ˜ ํ‘œํ˜„
์„ ํ˜•๊ฒฐํ•ฉ๊ณผ ์ƒ์„ฑ
์„ ํ˜•๋…๋ฆฝ์ด๋ž€?
์„ ํ˜•๋…๋ฆฝ ๋” ์•Œ์•„๋ณด๊ธฐ
์„ ํ˜•์ƒ์„ฑ๊ณผ ์„ ํ˜•๋…๋ฆฝ ์˜ˆ์ œ
์„ ํ˜• ๋ถ€๋ถ„๊ณต๊ฐ„
๋ถ€๋ถ„๊ณต๊ฐ„์˜ ๊ธฐ์ €
๋ฒกํ„ฐ์˜ ๋‚ด์ ๊ณผ ๋ฒกํ„ฐ์˜ ๊ธธ์ด
๋ฒกํ„ฐ ๋‚ด์ ์˜ ์„ฑ์งˆ ์ฆ๋ช…
์ฝ”์‹œ-์Šˆ๋ฐ”๋ฅด์ธ  ๋ถ€๋“ฑ์‹์˜ ์ฆ๋ช…
๋ฒกํ„ฐ์˜ ์‚ผ๊ฐ๋ถ€๋“ฑ์‹
๋ฒกํ„ฐ ์‚ฌ์ด์˜ ๊ฐ ์ •์˜ํ•˜๊ธฐ
์ ๊ณผ ๋ฒ•์„ ๋ฒกํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ R3์—์„œ ํ‰๋ฉด ์ •์˜ํ•˜๊ธฐ
ํ–‰๋ ฌ ๋ฒกํ„ฐ์˜ ๊ณฑ
ํ–‰๋ ฌ์˜ ์˜๊ณต๊ฐ„์ด๋ž€?
์˜๊ณต๊ฐ„ 2 : ํ–‰๋ ฌ์˜ ์˜๊ณต๊ฐ„ ๊ณ„์‚ฐํ•˜๊ธฐ
์˜๊ณต๊ฐ„ 3 : ์„ ํ˜• ๋…๋ฆฝ๊ณผ์˜ ๊ด€๊ณ„
ํ–‰๋ ฌ์˜ ์—ด๊ณต๊ฐ„
์˜๊ณต๊ฐ„๊ณผ ์—ด๊ณต๊ฐ„์˜ ๊ธฐ์ €
R3์— ์žˆ๋Š” ์—ด๊ณต๊ฐ„์„ ํ‰๋ฉด์œผ๋กœ ์‹œ๊ฐํ™”ํ•˜๊ธฐ
์ฆ๋ช… : ์–ด๋– ํ•œ ๋ถ€๋ถ„๊ฐ„์˜ ๊ธฐ์ €๋„ ์›์†Œ์˜ ์ˆ˜๊ฐ€ ๊ฐ™์Šต๋‹ˆ๋‹ค.
์˜๊ณต๊ฐ„์˜ ์ฐจ์›
์—ด๊ณต๊ฐ„์˜ ์ฐจ์›
๊ธฐ์ € ์—ด๊ณผ ์ถ•์—ด๊ณผ์˜ ๊ด€๊ณ„
ํ›„๋ณด ๊ธฐ์ €์˜ A์˜ ์—ด๊ณต๊ฐ„ ์ƒ์„ฑ
ํ•จ์ˆ˜ ๋” ์ดํ•ดํ•˜๊ธฐ
๋ฒกํ„ฐ์˜ ๋ณ€ํ™˜
์„ ํ˜•๋ณ€ํ™˜
์‹œ๊ฐ์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ธ ๋ณ€ํ™˜์—์„œ์˜ ํ–‰๋ ฌ
์„ ํ˜• ๋ณ€ํ™˜์˜ ํ–‰๋ ฌ ๋ฒกํ„ฐ์ 
ํ–‰๋ ฌ ๋ฒกํ„ฐ์ ์˜ ์„ ํ˜•๋ณ€ํ™˜
๋ณ€ํ™˜์—์„œ์˜ ๋ถ€๋ถ„์ง‘ํ•ฉ์˜ ์ƒ
Im(T): ๋ณ€ํ™˜์˜ ์ƒ
์ง‘ํ•ฉ์˜ ์›์ƒ
์›์ƒ๊ณผ ํ•ต ์˜ˆ์ œ
์„ ํ˜•๋ณ€ํ™˜์˜ ํ•ฉ๊ณผ ์Šค์นผ๋ผ ๊ณฑ
ํ–‰๋ ฌ์˜ ํ•ฉ๊ณผ ์Šค์นผ๋ผ ๊ณฑ ์‹ฌํ™”
์„ ํ˜•๋ณ€ํ™˜ ์˜ˆ์ œ: ์Šค์ผ€์ผ ๋ณ€ํ™˜๊ณผ ๋ฐ˜์‚ฌ
์„ ํ˜•๋ณ€ํ™˜ ์˜ˆ์ œ: R2์—์„œ์˜ ํšŒ์ „
R3์—์„œ x์ถ•์„ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ํšŒ์ „
๋‹จ์œ„๋ฒกํ„ฐ
์ •์‚ฌ์˜์ด๋ž€?
ํ–‰๋ ฌ ๋ฒกํ„ฐ์ ์œผ๋กœ ์ง์„ ์— ์ •์‚ฌ์˜ ๋‚˜ํƒ€๋‚ด๊ธฐ
์„ ํ˜•๋ณ€ํ™˜์˜ ํ•ฉ์„ฑ 1
์„ ํ˜•๋ณ€ํ™˜์˜ ํ•ฉ์„ฑ 2
ํ–‰๋ ฌ๊ณฑ ์˜ˆ์ œ
ํ–‰๋ ฌ๊ณฑ์˜ ๊ฒฐํ•ฉ๋ฒ•์น™
ํ–‰๋ ฌ๊ณฑ์˜ ๋ถ„๋ฐฐ๋ฒ•์น™
ํ•จ์ˆ˜์˜ ์—ญ์ด๋ž€?
์ฆ๋ช…: ๊ฐ€์—ญ์„ฑ์€ f(x)=y ์˜ ํ•ด๊ฐ€ ์œ ์ผํ•จ์„ ์˜๋ฏธํ•ฉ๋‹ˆ๋‹ค.
์ „์‚ฌํ•จ์ˆ˜์™€ ๋‹จ์‚ฌํ•จ์ˆ˜
๊ฐ€์—ญ์„ฑ๊ณผ ์ „์‚ฌํ•จ์ˆ˜, ๋‹จ์‚ฌํ•จ์ˆ˜์˜ ๊ด€๊ณ„
๋ณ€ํ™˜์ด ์ „์‚ฌํ•จ์ˆ˜์ธ์ง€ ํŒ๋ณ„ํ•˜๊ธฐ
Ax = b ์˜ ํ•ด์ง‘ํ•ฉ ๊ตฌํ•˜๊ธฐ
๋‹จ์‚ฌ๋ณ€ํ™˜์— ๋Œ€ํ•œ ํ–‰๋ ฌ์˜ ์กฐ๊ฑด
๊ฐ€์—ญ์„ฑ์˜ ์กฐ๊ฑด์„ ๊ฐ„๋‹จํžˆํ•˜๊ธฐ
์—ญ๋ณ€ํ™˜์ด ์„ ํ˜•์ž„์„ ํ™•์ธํ•˜๊ธฐ