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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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  1. 2020 TIL
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2 Wed

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Last updated 4 years ago

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ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค AI ์Šค์ฟจ 1๊ธฐ

1์ฃผ์ฐจ DAY 2

์˜ค๋Š˜ ์•ˆ์— DAY 2์™€ DAY 3๋ฅผ ๋๋‚ด๊ฒ ๋‹ค ๋‹ค์งํ–ˆ๊ฑด๋งŒ, DAY 2๋ฅผ ๋‹ค ํ’€์ง€ ๋ชปํ–ˆ๋‹ค. 12์‹œ๊นŒ์ง€ ํ•ด์•ผ ์ถœ์„์— ๋ฐ˜์˜๋œ๋‹ค๊ณ  ํ–ˆ๋Š”๋ฐ ํฐ์ผ๋‚œ ๊ฒƒ ๊ฐ™๋‹ค. ์ž์ •์„ ๋„˜๊ฒจ์„œ๋ผ๋„ ์–ผ๋ฅธ ํ•ด์•ผ๊ฒ ๋‹ค.

DAY 1 ์—์„œ๋Š” ๋„ˆ๋ฌด ๊ธฐ์ดˆ์ ์ธ ๊ฒŒ ์•„๋‹Œ๊ฐ€ ์‹ถ์—ˆ๋Š”๋ฐ, DAY 2๋Š” ์ข€ ๋” ์œ ์ตํ–ˆ๋‹ค.(ํ˜น์‹œ, ๋‚ด ์ˆ˜์ค€์€ ์—ฌ๊ธฐ์„œ ๋ถ€ํ„ฐ์ธ ๊ฑธ๊นŒ?) ์ค‘์œ„ํ‘œํ˜„์˜ ํ›„์œ„ํ‘œํ˜„ ๋ถ€ํ„ฐ ์‰ฝ์ง€ ์•Š์•˜๋˜ ๊ฒƒ ๊ฐ™๋‹ค. ํ™•์‹คํžˆ ์ฒ˜์Œ ๋ฐฐ์šธ ๋•Œ๋„ ๊ทธ๋žฌ์ง€๋งŒ, ์ดํ•ด ํ•˜๋Š” ๊ฒƒ๊ณผ ๊ตฌํ˜„ํ•˜๋Š” ๊ฒƒ์€ ์ •๋ง ๋‹ค๋ฅธ ๊ฒƒ ๊ฐ™๋‹ค. ์ด๋Ÿด ๊ธฐํšŒ๊ฐ€ ์‹ค์ œ ๋Œ€ํ•™ ๊ฐ•์˜ ์ด์™ธ์—๋Š” ์—†์—ˆ๋Š”๋ฐ, ์ด๋ ‡๊ฒŒ ๋‹ค์‹œ ๊ตฌํ˜„ํ•ด๋ณด๋‹ˆ๊นŒ ํฐ ๋ณต์Šต์ด ๋˜๋Š” ๊ฒƒ ๊ฐ™๋‹ค. ์ƒ๊ฐ๋ณด๋‹ค ์–ด๋ ค์›Œ์„œ ์‹ค์Šต ํ•˜๋‚˜ ํ’€๊ธฐ๊ฐ€ ์‰ฝ์ง€ ์•Š๋‹ค. ๋‹คํ–‰ํžˆ๋„ ๋ชจ๋“  ์ž๋ฃŒ๊ตฌ์กฐ์™€ ๋™์ž‘ ๋ฐฉ์‹์„ ๊ฐ„๋‹จํ•˜๊ฒŒ๋ผ๋„ ์•Œ๊ณ  ์žˆ์–ด์„œ ํก์ˆ˜๋Š” ๋ฐ”๋กœ๋ฐ”๋กœ ํ–ˆ๋‹ค. ๋ช‡ ๊ฐœ ๋‚˜๋ฆ„ ์ค‘์š”ํ•˜๋‹ค๊ณ  ์ƒ๊ฐํ•œ ๋‚ด

  • ์šฐ์„ ์ˆœ์œ„ ํ๋Š” ์‚ญ์ œ ํ•  ๋•Œ๋งˆ๋‹ค ํ•ด๋‹น ๋ฐ์ดํ„ฐ๋ฅผ ์ฐพ๋Š” ๊ฒƒ๋ณด๋‹ค ์‚ฝ์ž… ํ•  ๋•Œ๋งˆ๋‹ค ๋ฐ์ดํ„ฐ๋ฅผ ์ •๋ ฌํ•˜๋Š” ๊ฒƒ์ด ๋” ๋น ๋ฅด๋‹ค.

  • ์šฐ์„ ์ˆœ์œ„ ํ๋Š” ์„ ํ˜• ๋ฐฐ์—ด๋กœ ๊ตฌํ˜„ํ•  ๊ฒฝ์šฐ ๋ฉ”๋ชจ๋ฆฌ์—์„œ, ์—ฐ๊ฒฐ ๋ฆฌ์ŠคํŠธ๋กœ ๊ตฌํ˜„ํ•  ๊ฒฝ์šฐ ์†๋„์—์„œ ๊ฐ•์ ์„ ์–ป๋Š”๋‹ค.

1์ฃผ์ฐจ DAY 3

dictionary์˜ ์ดˆ๊ธฐํ™”์— ๋Œ€ํ•ด์„œ d.setdefault(x, 0)๋งŒ ์•Œ๊ณ  ์žˆ์—ˆ๋Š”๋ฐ, d.get(x, 0)์˜ ๋ฌธ๋ฒ•์„ ์•Œ๊ฒŒ ๋˜์—ˆ๋‹ค. ๋‘˜์˜ ์ฐจ์ด์ ์„ ์ข€ ๋” ์ž์„ธํžˆ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ๋‹ค์Œ ๋งํฌ ์ฐธ

get๋ณด๋‹ค setdefault์˜ ์†๋„๊ฐ€ 10% ๋” ๋น ๋ฅด๋‹ค. ๋‹ค๋งŒ, setdefault๋Š” unset๋œ dictionary์— ๋Œ€ํ•ด์„œ ์ดˆ๊ธฐํ™”๋ฅผ ํ•˜๋Š” ๊ฒƒ์ด๊ณ  get์€ unset์„ ๊ตฌ๋ถ„์ง“์ง€ ์•Š๊ณ  reset์„ ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ํŠน์ง•์ด ์žˆ๋‹ค.

์ „๋ฐ˜์ ์œผ๋กœ ์œ ๋ฃŒ ๊ฐ•์˜์— ๋Œ€ํ•ด ๋А๋‚€ ์ ์€, ์–ด๋–ป๊ฒŒ ํ’€์–ด์•ผ ํ• ์ง€๋ฅผ ์ž˜ ์„ค๋ช…ํ•˜๊ณ  ๋ฌธ์ œ ํ’€์ด๋ฅผ ์‹œ์ž‘ํ•˜๋Š” ๊ฐ•์˜๋ผ๊ณ  ์ƒ๊ฐํ•œ๋‹ค. ์‹ค์ œ ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค์—์„œ ๋ฌธ์ œ๋ฅผ ํ’€๋ฉด ๋‹ค๋ฅธ ์‚ฌ๋žŒ์˜ ์ฝ”๋“œ๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋Š”๋ฐ, ํ’€์ด ๋ฐฉ๋ฒ•์ด ์ถ”์ฒœ์„ ๋งŽ์ด ๋ฐ›์€ ํ’€์ด์— ๋Œ€๋ถ€๋ถ„ ์กด์žฌํ•œ๋‹ค. ๊ทธ ์‚ฌ๋žŒ๋“ค์ด ์œ ๋ฃŒ๊ฐ•์˜๋ฅผ ๋ณด๊ณ  ํ’€์€ ๊ฒƒ์ธ์ง€, ์ด ์‚ฌ๋žŒ๋“ค์˜ ํ’€์ด๋ฅผ ๋ณด๊ณ  ์œ ๋ฃŒ๊ฐ•์˜ ์ œ์ž‘์— ์ฐธ๊ณ ํ•œ ๊ฒƒ์ธ์ง€๋Š” ๋ชจ๋ฅด๊ฒ ๋‹ค.(์•„๋งˆ ํ›„์ž๊ฐ€ ์•„๋‹๊นŒ). ๋‹ค๋งŒ ์•Œ๋ ค์ค€ ํ’€์ด ๋ฐฉ๋ฒ•๋ณด๋‹ค ๋” ์ข‹์€ ๋ฐฉ๋ฒ•๋„ ๋ช‡ ๊ฐœ ์žˆ์œผ๋‹ˆ ๊ฐ•์˜๋ฅผ ๋ณด๋Š” ์‚ฌ๋žŒ๋“ค์€ ๋” ๊ฐœ์„ ํ•  ์ ์„ ์ƒ๊ฐํ•ด๋ณด๋ฉด ์ข‹์„ ๊ฒƒ ๊ฐ™๋‹ค.

๋˜, AI ์Šค์ฟจ 1์ฃผ์ฐจ์— ๋Œ€ํ•ด ๋А๋‚€ ์ ์€ ์ด๋ฏธ CS ์ง€์‹์— ๋Œ€ํ•ด ์–ด๋А ์ •๋„ ์•Œ๊ณ  ์žˆ์œผ๋ฉด ๋ณต์Šต์ด ๋˜๊ณ  ๋ณด์ถฉ์ด ๋  ๊ฒƒ ๊ฐ™๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ตœ๊ทผ์— ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค์˜ ์—ฌ๋Ÿฌ ๋ฌธ์ œ๋ฅผ ํ’€์ดํ–ˆ๋˜ ์‚ฌ๋žŒ๋“ค์€ ๋ฌธ์ œ ํ’€์ด์— ๋Œ€ํ•ด์„œ๋Š” ์ข€ ์‹œ๊ฐ„ ์ฑ„์šฐ๊ธฐ ๋А๋‚Œ์ด ๋“ค ๊ฒƒ ๊ฐ™๋‹ค. (๋‚ด๊ฐ€ ํ›„์ž์˜ ๋А๋‚Œ์ด๋ผ...) ํ”ผ๋“œ๋ฐฑ์ด ์žˆ๋‹ค๋ฉด, ์•„์ง AI๋ฅผ ๋ฐฐ์šฐ๊ธฐ ์ „์ด์ง€๋งŒ ์ด๋Ÿฌํ•œ ๋ฌธ๋ฒ•์€ ์–ด๋–ค ๊ฒƒ์„ ๋ฐฐ์šฐ๋Š” ๋ฐ ํ•„์š”ํ•˜๋‹ค ๊ฐ™์€ ์ ์šฉ ๋ถ„์•ผ๋ฅผ ์–ธ๊ธ‰ํ•˜๊ฑฐ๋‚˜ ๊ฐ„๋‹จํ•œ AI ์ง€์‹์„ ์ ์šฉํ•˜๋ฉด์„œ ์ด๋ ‡๊ฒŒ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค ๋ผ๋Š” ๋ฐฉ์‹์œผ๋กœ ๋ฐฐ์šฐ๋ฉด ์ข€ ๋” ํฅ๋ฏธ๋กœ์šธ ๊ฒƒ ๊ฐ™๋‹ค.

์˜คํ† ๋งˆํƒ€์™€ ์ปดํŒŒ์ผ๋Ÿฌ

์‹ค์ˆ˜ ํŒ๋ณ„ ํ† ํฐ ๋ถ„์„

์ˆ˜๊ฐ• ์ค‘์ธ ๊ฐ•์˜์— ์‹ค์ˆ˜๋ฅผ ํŒ๋ณ„ํ•˜๋Š” ์˜คํ† ๋งˆํƒ€์˜ DFA๋ฅผ ๊ทธ๋ฆฌ๊ณ  ์‹ค์ œ๋กœ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ํ•˜๋Š” ๊ณผ์ œ๊ฐ€ ๋‚˜์™”๋‹ค. ์ •๊ทœ์‹์œผ๋กœ ๋น ๋ฐฉ ํ•˜๋‹ˆ๊นŒ 5์ค„๋งŒ์— ๋๋‚ฌ๋Š”๋ฐ, ํ† ํฐ์„ ๋ถ„์„ํ•˜๋Š” ๋А๋‚Œ์ด ์•„๋‹ˆ์–ด์„œ(๋ฌผ๋ก  ์ •๊ทœ์‹ ๋‚ด๋ถ€์—์„œ๋Š” ํ† ํฐ์œผ๋กœ ๋ถ„์„ํ•˜๊ฒ ์ง€๋งŒ ๋‚ด๊ฐ€ ํ† ํฐ๋งˆ๋‹ค ๋ถ„์„ํ•œ ๊ฑด ์•„๋‹ˆ๋‹ˆ๊นŒ) ์—ฌ๋Ÿฌ ๋ถ„๊ธฐ์ ์„ ๋งŒ๋“ค์—ˆ๋‹ค.

#
#   data : 2020.12.02
#   author : sangmandu at Dankook Univ.
#   program : automata that analyze token by token and distinguish type of input is float or not
#


#
# prerequisite : the number could not be calculated by other operators or operand
# although there are many operators and operand and result of calculation number is regarded as certain type,
# here's token analyzer regards input as calculated number completely
# ex) -11-3.e-3 is float but token analyzer says this is no float as duplicate of '-'
#


# Test cases
X = ['+5-5', '5*5*5', '5-5-5', '1...5', '1.2.3', '1.5e15', '000.5', '0000.', '000100.5', '3.5', '2', '4.', '-5.3E+2', '36', '-52', '-13.E+3', '54.123E-2',
     '0', '0.0', '-0', '-0.0', '.35', '+++++3.5', '-----3.5','11.e+++3', '11.e---3', '11.e-3-', 'abc', '+35a', '-35b', '+35.5a', '123a.123', '23.12c', '11.232e++']
Y = [False, False, False, False, False, True, True, True, True, True, False, True, True, False, False, True, True,
     False, True, False, True, True, True, True, False, False, False, False, False, False, False, False, False, False]
P = []

#
# Float Regular Expression
# Re : [+-]?([0-9]+(\.[0-9]*)?|\.[0-9]+)([eE][+-]?[0-9]+)?
#
# 0 : [+-]      1 : [0-9]       4 : [Ee]        5 : (Ee)[0-0]
# 2 : (exist)[.](free num)      3 : (none)[.](essential num)
#
# () : able to skipped, -- : essential, == : optional
# order one : (0) -- 1 -- 2 == 4 -- (0) -- 5
# order two : (0) -- 3 == 4 -- (0) -- 5
#

def floatOrNot(x):
    idxRe = 0
    for idx, token in enumerate(x):
        if token in ['+', '-']:         # sign check
            if idxRe == 0:
                continue
            elif idxRe == 4:
                idxRe = 5
                continue
            else:
                return (False, f"unproper location of {token}")

        if token in '0123456789':       # number check
            if idxRe == 0:
                idxRe = 1
                continue
            elif idxRe in [1, 2, 3, 5]:
                continue
            elif idxRe == 4:
                idxRe = 5
                continue

        if token == '.':                # dot check
            if idxRe == 0:
                idxRe = 3
                continue
            elif idxRe == 1:
                idxRe = 2
                continue
            else:
                return (False, "Alreay being dot")

        if token in ['e', 'E']:         # exponential notation check
            if idxRe in [2, 3]:
                idxRe = 4
                continue
            else:
                return (False, "No dot or No num")

        return (False, f"Not number {token}")               # no matching
    return (True, ) if idxRe > 1 else (False, "Int")        # float must have dot

#
# function floatOrNot return only True value as tuple type when input is float.
# but when input is not float then return False value and why False
#

X = list(map(str, X))
P = sum([[floatOrNot(x)] for x in X], [])
correct = [y == p[0] for y, p in zip(Y, P)]
accuracy = correct.count(True) / len(correct)

print(
    f"\nPerformance is {accuracy*100}%\n\nuncorrect result(case, label) is", end=''
)
if int(accuracy*100) == 100:
    print(" None.\n")
    print("[Result]")
    for i in range(len(P)):
        print("%10s"%str(X[i])+"%10s"%str(P[i][0])+"\t"+(str(P[i][1]) if P[i][0] != True else ''))
else:
    print()
    for i in range(len(correct)):
        if correct[i] != True:
            print(f"{[(X[i], Y[i], P[i]) ]}")

๋”ฑ 100์ค„ ์ฝ”๋“œ(์ฃผ์„์ด ์ข€ ๋งŽ๊ธด ํ•˜์ง€๋งŒ ํ•˜ํ•˜..) ์ถœ๋ ฅ ๊ฒฐ๊ณผ๋„ ๊น”๋”ํ•˜๊ฒŒ ํ–ˆ๋‹ค. ์ž˜ ๊ฒฐํ•œ ๋“ฏ ์‹ถ๋‹ค.

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