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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. ์ผ์ผ ์ปค๋ฐ‹
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  1. : Retrospective
  2. 21Y

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210702

Previous6M5WNext์ƒˆ์‚ฌ๋žŒ ๋˜๊ธฐ ํ”„๋กœ์ ํŠธ

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2021๋…„ ์ƒ๋ฐ˜๊ธฐ ํšŒ๊ณ 

์•ž์œผ๋กœ๋Š” ๋ถ„๊ธฐ๋ณ„๋กœ ์“ธ ๊ณ„ํš์ด๋‹ค. ํ•˜๋ฐ˜๊ธฐ ํšŒ๊ณ ๊ฐ€ ์—†์–ด์„œ ์ง์ด ์—†๋Š” ์œ ์ผํ•œ ์ƒ๋ฐ˜๊ธฐ ํšŒ๊ณ ๋กœ ๋‚จ์•„๋ผ.

์ƒ๋ฐ˜๊ธฐ ๋‚ด๊ฐ€ ์–ด๋–ป๊ฒŒ ๋ณด๋ƒˆ๋Š”์ง€๋ฅผ ์ •๋ฆฌํ•˜๋ ค๊ณ  ํ•œ๋‹ค.

์ฒซ ํšŒ๊ณ 

ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค ์Šค์ฟจ๋•์— ์‹œ์ž‘ํ•œ TIL. ์ด์ „์— ์ฐธ๊ณ ํ•œ ๋งŽ์€ TIL์„ ๋ณด๋ฉด ๊ผญ ํšŒ๊ณ ๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋‹จ์ง€ ๋А๋‚€์  ํ˜น์€ ์ผ๊ธฐ๊ฐ€ ๋  ๊ฒƒ ๊ฐ™์•„์„œ ์จ์•ผ๋œ๋‹ค๋Š” ์˜์‹์€ ์—†์—ˆ๋‹ค. ์“ฐ๊ณ  ์‹ถ์€ ๋งˆ์Œ์ด ์กฐ๊ธˆ ์žˆ์—ˆ์ง€๋งŒ ์–ด์ค์ž–๊ฒŒ ๋ฏธ๋ฃจ๊ณ  ์žˆ์—ˆ๋Š”๋ฐ, ์ด๋ถ„์˜ TIL ํšŒ๊ณ ๋ฅผ ๋ณด๊ณ  ์ •๋ฆฌ์™€ ๋ฐ˜์„ฑ ๊ทธ๋ฆฌ๊ณ  ๋‚˜์•„๊ฐ€๊ธฐ ์œ„ํ•œ ๋ฐœํŒ์ด ๋  ์ˆ˜ ์žˆ๋‹ค๊ณ  ๋А๊ผˆ๋‹ค. ์ด ํšŒ๊ณ ์˜ ๋ฐฉ์‹์„ ๋™์ผํ•˜๊ฒŒ ๋”ฐ๋ผ๊ฐ€๋ ค๊ณ  ํ•œ๋‹ค.

๊ฐœ๋ณ„ ์—ญ๋Ÿ‰

1. ์ผ์ผ ์ปค๋ฐ‹

TIL์„ ํ•˜๋ฉด์„œ ๊ฐ€์žฅ ํ•˜๊ณ  ์‹ถ์—ˆ๋˜ ๋ชฉํ‘œ์ด๋‹ค. ์ด๊ฒƒ ๋•Œ๋ฌธ์— ํ•œ๊ธ€ ํ˜ธํ™˜๊ณผ ๊ฐ์ข… ๋ฒ„๊ทธ์™€ ์‹ฌํ•œ ๋ ‰์„ ๊ฐ์ˆ˜ํ•˜๊ณ ๋„ ๊นƒ๋ถ์„ ์“ฐ๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด๋Ÿด ์ค„ ์•Œ์•˜์œผ๋ฉด ๊นƒํ—ˆ๋ธŒ ์•„์ด์˜ค ์“ธ๊ฑธ. ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค ์Šค์ฟจ์„ ๋“ค์œผ๋ฉด์„œ ๋งŽ์€ TIL์„ ์ž‘์„ฑํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ , ์ธํ”„๋Ÿฐ์ด๋ผ๋Š” ๊ฐ•์˜ ์‚ฌ์ดํŠธ๋ฅผ ์•Œ๊ฒŒ๋˜๋ฉด์„œ, ๊ทธ๋ฆฌ๊ณ  ๊ทธ ์™ธ์˜๋„ ์œ ํŠœ๋ธŒ๋“ฑ์˜ ๋งŽ์€ ๊ฐ•์˜๋ฅผ ์•Œ๊ฒŒ๋˜์—ˆ๋˜ ๊ฒƒ๋„ ํ•œ๋ชซํ•œ ๊ฒƒ ๊ฐ™๋‹ค.

๋ฌผ๋ก , 1์ผ 1์ปค๋ฐ‹์„ ๊พธ์ค€ํžˆ ํ–ˆ๋ƒ๋Š” ๋ง์—๋Š” ๊ฑฐ์ง“ ์ด๋ผ๊ณ  ๋งํ•˜๊ฒ ๋‹ค. ์ฃผ๋กœ ์ฃผ๋ง์— ๋นˆ๋‘ฅ๊ฑฐ๋ฆฌ๋Š” ๋ชจ์Šต์ด ์žˆ์—ˆ๊ณ  2์ฃผ์— ํ•˜๋ฃจ ์ •๋„๋Š” ์ž”๋”” ๋ฉ”๊พธ๊ธฐ๋ฅผ ํ–ˆ๋˜ ๊ฒƒ ๊ฐ™๋‹ค. ๊ทธ๋ž˜๋„ ์—ด์‹ฌํžˆ ํ•˜๊ธด ํ–ˆ์ง€๋งŒ, ์™„๋ฒฝํ•˜๊ฒŒ ์ฑ„์šฐ๊ณ  ์‹ถ์€ ๋งˆ์Œ์— ๋ฉ”๊พธ๊ธฐ๋ฅผ ๋ฉˆ์ถœ ์ˆ˜ ์—†์—ˆ๋‹ค. ๋นˆ๊ณต๊ฐ„์ด ์ƒ๊ธฐ๋ฉด ์˜์š•์„ ์žƒ์„๊นŒ๋ด ๊ทธ๋ฆฌ๊ณ  ์ž๊ทน์„ ๋ฐ›๊ธฐ์œ„ํ•ด์„œ ๊ทธ๋ž˜์™”๋˜ ๊ฒƒ ๊ฐ™๋‹ค. 7์›”๋ถ€ํ„ฐ๋Š” TIL ์“ฐ๋Š” ๋ฐฉ์‹์„ ๋ฐ”๊พธ๊ธฐ๋„ ํ–ˆ๊ณ , ํŽ˜์ด์ง€๋ฅผ ์ „๋ฐ˜์ ์œผ๋กœ ์—…๋ฐ์ดํŠธ ํ•ด์„œ ๋”์ด์ƒ์˜ ์ž”๋”” ๋ฉ”๊พธ๊ธฐ๋ฅผ ํ•˜์ง€ ์•Š๊ฒ ๋‹ค๊ณ  ๋‹ค์งํ–ˆ๋‹ค.

1์ผ 1์ปค๋ฐ‹, ํŠนํžˆ 1TIL์€ ์˜์‹ํ•ด์„œ ์จ์•ผํ•œ๋‹ค. ๊ณต๋ถ€ํ•˜๊ธฐ ์‹ซ์€ ๋‚ ๋„, ๋†€๋Ÿฌ๊ฐ€๋Š” ๋‚ ๋„ ์žˆ๋Š”๋ฐ ๊ทธ๋ž˜๋„ ์•„์ฃผ ์กฐ๊ธˆ์ด๋ผ๋„ ํ•˜๋Š” ๊ฒŒ ์ค‘์š”ํ•˜๋‹ค. ์š”์ฆ˜์€ ์ ์  ์Šต๊ด€์ด ๋˜๊ณ  ์žˆ๋Š” ๊ฒƒ ๊ฐ™๋‹ค.

2. ๊นƒ๋ถ

๊นƒ๋ถ ์—…๋ฐ์ดํŠธ๋ฅผ ์•ˆํ•œ๋‹ค. ํ”ผ๋“œ๋ฐฑ๋„ ์†Œ์šฉ์ด์—†๋‹ค. ์ž‘์„ฑํ•˜๋Š” ๊ฒŒ ๋„ˆ๋ฌด ํž˜๋“ ๋ฐ, ๊ทธ๋ž˜๋„ ๊พธ์—ญ๊พธ์—ญ ํ•œ๋‹ค. ํ•ด๋†“์€๊ฒŒ ์•„๊น๊ธฐ๋„ ํ•˜๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์™ผ์ชฝ์— ๋ชฉ์ฐจ๊ฐ€ ๊น”๋”ํ•˜๊ฒŒ ๋˜์–ด์žˆ๋Š” ๊ฒŒ ๋„ˆ๋ฌด ์ข‹๋‹ค. ๋‹ค๋งŒ ํ—ค๋” ํƒœ๊ทธ๊ฐ€ 3๊ฐœ๋ฐ–์— ์—†๋Š” ์ , ๊ธ€์”จ ์ƒ‰์ด ๊ฒ€์ •์ด ์œ ์ผ ๋“ฑ๋“ฑ์ด ๋‚˜์—๊ฒŒ๋Š” ๋‹จ์ 

๊นƒ๋ถ์„ ์“ฐ๋‹ค ํ™”๊ฐ€๋‚˜์„œ ๋ฉฐ์น ์„ ์—ฐ๊ตฌํ•˜๊ณ  TIL ํ•˜๋‚˜๋ฅผ ๊นƒ๋ถ์˜ ๋‹จ์ ์œผ๋กœ ์“ด ์ ์ด ์žˆ๋Š”๋ฐ ์ง€๊ธˆ์€ ๊ทธ๋Ÿฌ๋ ค๋‹ˆ ํ•œ๋‹ค.

3. ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค AI ์Šค์ฟจ

๋‚˜์—๊ฒŒ ๋ณ€ํ™”๋ฅผ ์ค€ ํ”„๋กœ๊ทธ๋žจ์ด๋‹ค. ๊นƒํ—ˆ๋ธŒ๋ฅผ ์ข€ ๋” ๋งŽ์ด ์“ฐ๊ฒŒ ๋˜์—ˆ๊ณ , ๋ฐฐ์›€์˜ ๋ฐฉํ–ฅ์„ ์„ธ์›Œ์ฃผ์—ˆ์œผ๋ฉฐ TIL์„ ์‹œ์ž‘ํ•˜๊ฒŒ ํ•œ.

๋‚˜์—๊ฒŒ ๋ฏธ์นœ ์˜ํ–ฅ์€ ์ด์ •๋„์ด๋‹ค. ๊ทธ ์ด์™ธ์—๋Š” ํฐ ๋„์›€์ด ๋˜์ง€ ๋ชปํ–ˆ๋˜ ๊ฒƒ ๊ฐ™๋‹ค. ํ”„๋กœ์ ํŠธ๋„ ๋™์•„๋ฆฌ ํ”„๋กœ์ ํŠธ ๋งˆ๋ƒฅ ๋๋‚œ ๋А๋‚Œ์ด ๋‹ค๋ถ„ํ–ˆ๊ณ  ์ƒ๊ฐํ–ˆ๋˜ ์—ฌ๋Ÿฌ ๋™๋ฃŒ์™€ ์ „๋ฌธ๊ฐ€์™€์˜ ๋„คํŠธ์›Œํ‚น๋„ ์—†์—ˆ๋‹ค. ๊ฒฐ์ •์ ์œผ๋กœ ์ง€์‹์ ์ธ ์ธก๋ฉด์—์„œ ๋งŽ์€ ๊ฒƒ์„ ์–ป์ง€ ๋ชปํ•œ ์Šค์ฟจ.

ํ›„ํšŒ๋Š” ์—†์ง€๋งŒ ์•„์‰ฌ์›€์€ ๋งŽ๋‹ค. ์ด๊ฑฐ ๋“ฃ๋А๋ผ๊ณ  ๋ถ€์ŠคํŠธ์บ ํ”„ ํ•ฉ๊ฒฉ์„ ํฌ๊ธฐํ–ˆ๋Š”๋ฐ ์ด๊ฑด ํ›„ํšŒํ•œ๋‹ค. ์ง€๊ธˆ์€ ๋‹ค์‹œ ์ง€์›ํ•ด์„œ ๋‚ด์ผ 2์ฐจ ์ฝ”ํ…Œ๋งŒ์„ ์•ž๋‘์—ˆ๋‹ค. ๊ผญ ๋ถ™์—ˆ์œผ๋ฉด!

4. TIL

๋‚˜๋Š” ํ‹ธ์— ๋Œ€ํ•ด์„œ ๊ต‰์žฅํžˆ ์• ์ •์ด ์žˆ๋Š”๋ฐ, ์ž˜ ๊ตฌ์„ฑํ–ˆ๋‹ค๊ณ  ์ƒ๊ฐํ•˜๋Š” ์ž๋ถ€์‹ฌ์ด ์žˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค๋ฉด,

์ด๋Ÿฐ์‹์œผ๋กœ ํ˜„์žฌ ๊ณต๋ถ€์ค‘์ธ ๋‚ด์šฉ๋“ค์„ ๊ทธ๋ž˜ํ”„๋กœ ์ •๋ฆฌํ•˜๊ณ  ๊ฐ ๊ทธ๋ž˜ํ”„๋งˆ๋‹ค ๋งํฌํ™” ๋˜์–ด ํ•ด๋‹น ๊ณต๋ถ€์— ๋Œ€ํ•œ ๊ธฐ๋ก์„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค๋Š” ์ . ๋˜,

๊ณต๋ถ€ํ•œ ๋‚ด์šฉ์„ ๋ชฉ์ฐจ๋ณ„๋กœ ๋˜ ์ •๋ฆฌํ•ด์„œ ๋ณผ ์ˆ˜ ์žˆ๋Š”์ .(๊ทธ๋ž˜ํ”„๋กœ๋Š” ๋“ค์–ด๊ฐ€๊ธฐ ์ „๊นŒ์ง€ ์„ธ๋ถ€์‚ฌํ•ญ์„ ์•Œ ์ˆ˜ ์—†์œผ๋‹ˆ)

๋‚จ๋“ค์ด ๋ณด์•˜์„ ๋•Œ ์ฐธ์‹ ํ•˜๊ณ  ๊น”๋”ํ•˜๋‹ค ๋ผ๋Š” ํ‰์„ ๋“ค์œผ๋ฉด ์ข‹์„ ๊ฒƒ ๊ฐ™๋‹ค. ๊ทธ๋ž˜ํ”„๋กœ TIL์„ ๋งŒ๋“  ๊ฑด ์ง„์งœ ์ž๋ถ€์‹ฌ์ด ์žˆ๋‹ค.

๋ฐ˜๋Œ€๋กœ, ์ด๊ฑธ ์–ธ์ œ๊นŒ์ง€ ๊ณ„์† ํ• ๊นŒ? ๋ผ๋Š” ์˜๋ฌธ์ด ๋“ค ๋•Œ๋„ ์žˆ๋‹ค. ์ทจ์—…์— ์„ฑ๊ณตํ•˜๋ฉด ๊ทธ๋งŒ ๋‘์ง€ ์•Š์„๊นŒ? ์ด๋Ÿฐ ๋ฅ˜์˜. ๊ทธ๋ฆฌ๊ณ  ์ •๋ฆฌ๋ฅผ ์ž˜ํ•œ ํฌ์ŠคํŒ…๋„ ์žˆ์ง€๋งŒ ์ž˜ํ•˜์ง€ ๋ชปํ•œ ํฌ์ŠคํŒ…์ด ๋” ๋งŽ๋‹ค๊ณ  ์ƒ๊ฐํ•œ๋‹ค. ๊ทธ๋ž˜์„œ ์ด๊ฒƒ์ด ๋‚ด๊ฐ€ ์ฐธ๊ณ ํ•  ์ˆ˜ ์žˆ๊ณ  ๋” ๋‚˜์•„๊ฐ€ ๋‚จ๋“ค์ด ์ฐธ๊ณ ํ•  ์ˆ˜ ์žˆ์„๊นŒ? ๋‹จ์ˆœํžˆ ๊ทธ๋ƒฅ ๋‚˜ ์ด๊ฑฐ ๊ณต๋ถ€ํ–ˆ๋‹ค ํ•˜๋Š” ๊ธฐ๋ก์ •๋„์˜ ์˜๋ฏธ๋กœ๋งŒ ๋‚จ๋Š” ๊ฒƒ์ด ์•„๋‹Œ๊ฐ€? ๋ผ๋Š” ํšŒ์˜๊ฐ์ด ๋“ค ๋•Œ๋„ ์žˆ๋‹ค. ์ฒ˜์Œ์—๋Š” ๋งค์ผ ์ž‘์„ฑํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•œ๊ฑฐ์ง€ ๋ผ๋Š” ์ƒ๊ฐ์ด์—ˆ๋Š”๋ฐ, ์ง€๊ธˆ์€ ๋‚จ๋“ค์ด ๋‚ด ๊ธ€์„ ๋ดค์„ ๋•Œ ์ดํ•ด๊ฐ€ ๋˜์•ผ ํ•˜์ง€ ์•Š์„๊นŒ ํ•˜๋Š” ์˜ค์ง€๋ž–๊นŒ์ง€ ๋ถ€๋ฆด ๋•Œ๊ฐ€ ์žˆ๋‹ค. ์ด ๋ถ€๋ถ„์€ ๋Š˜ ๊ณ ๋ฏผ์ด๋‹ค.

5. ์•Œ๊ณ ๋ฆฌ์ฆ˜

ํ˜„์žฌ ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค ์ฝ”ํ…Œ ๋ฌธ์ œ๋ฅผ ๋ ˆ๋ฒจ 3์„ ํ’€๊ณ ์žˆ๋‹ค. ๊ฑฐ์˜ ๋‹ค ํ’€์–ด๊ฐ€๊ณ  ์žˆ๋Š”๋ฐ, ๊ทธ๋ž˜๋„ ์•„์ง ๊ทธ๋ž˜ํ”„๋‚˜ ํŠธ๋ฆฌ ๊ทธ๋ฆฌ๊ณ  DP์ชฝ์ด ๋„ˆ๋ฌด ์ทจ์•ฝํ•˜๋‹ค. ํ•œ๋ฒˆ ์‚ผ์„ฑ SDS ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ต์œก์„ ๋“ฃ๊ธฐ ์œ„ํ•ด ์ฝ”๋”ฉ ํ…Œ์ŠคํŠธ๋ฅผ ๋ณธ ์ ์ด ์žˆ๋Š”๋ฐ 5๊ฐœ์˜ ๋ฌธ์ œ์ค‘์— ๋‹จ ํ•˜๋‚˜๋งŒ 100์ . ํ•˜๋‚˜๋Š” 60์ . ๋‚˜๋จธ์ง€ 3๋ฌธ์ œ๋Š” ๋‹ค 0์ ์„ ๋งž์•˜๋‹ค. ๋‚˜๋Š” ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค ์ฝ”ํ…Œ ๋ฌธ์ œ๋ฅผ ์ž˜ ํ‘ธ๋Š”ํŽธ์ด๋ผ ๋‚˜๋ฆ„ ์ž๋ถ€์‹ฌ์ด ์žˆ์—ˆ๋Š”๋ฐ, ๋‹จ์ˆœํžˆ ๋กœ์ง์„ ์ƒ๊ฐํ•˜์ง€ ๋ชปํ•˜๋Š” ๊ฒŒ ์•„๋‹ˆ๋ผ ์‹œ๊ฐ„์ดˆ๊ณผ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜์ง€ ๋ชปํ•˜๋Š” ์–ด๋ ค์›€์„ ๋งž๋‹ฅ๋“ค์ด๋‹ˆ๊นŒ ์ž์‹ ๊ฐ์„ ์ข€ ์žƒ์—ˆ๋‹ค. SDS ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ต์œก์„ ๋“ฃ๊ธฐ์œ„ํ•œ ํ‹ฐ์ผ“์„ ๋”ฐ๊ธฐ๋Š” ํ–ˆ๋‹ค. ๋‹ค๋“ค ๋‚˜์ฒ˜๋Ÿผ ์–ด๋ ค์› ๋‚˜? ์•„๋‹ˆ๋ฉด ์ง€์›์ž๊ฐ€ ๋ณ„๋กœ ์—†์—ˆ๋‚˜.

ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค 4๋‹จ๊ณ„ ๊นŒ์ง€๋งŒ ํ’€์–ด๋ณด๊ณ  (5๋‹จ๊ณ„๋Š” ํ’€์ง€๋ง๋ผ๋Š” ์กฐ์–ธ์„ ๋“ค์—ˆ๋‹ค.) ๋ฐฑ์ค€์œผ๋กœ ๋„˜์–ด๊ฐ€๋ณด๊ณ  ์‹ถ๋‹ค. ๋‹ค๋“ค ํ‹ฐ์–ด ์ด์•ผ๊ธฐ๋ฅผ ๊ต‰์žฅํžˆ ๋งŽ์ดํ•˜๋Š”๋ฐ ํ”Œ๋ ˆ ์ •๋„๋ฉด ๊ธฐ์—… ์ฝ”ํ…Œ๋Š” ๊ฑฐ์˜ ๋‹ค ํ‘ผ๋‹ค๊ณ  ํ•˜๋‹ˆ, ๋‚˜๋„ ๋ฐฐ์น˜๊ณ ์‚ฌ์ข€ ๋ด์•ผ๊ฒ ๋‹ค. ํ˜„์žฌ ๋กคํ‹ฐ์–ด๋Š” ํ”Œ๋ ˆ3์ด๋‹ค. ๋นจ๋ฆฌ ์ ‘์–ด์•ผ ํ•˜๋Š”๋ฐ;;

6. ์Šคํ„ฐ๋””

ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค AI ์Šค์ฟจ์„ ํ•˜๋ฉด์„œ ๊ทธ๋ฆฌ๊ณ  ๋๋‚˜๊ณ  ๋‚œ๋’ค์— ๊ฐ•์˜๋„ ๋งŽ์ด ๋“ค์—ˆ๊ณ  ์ด๋ฅผ ์Šคํ„ฐ๋””๋ฅผ ํ†ตํ•ด ์ง„ํ–‰ํ•˜๋ ค๊ณ  ํ•œ ๊ฒƒ๋„ ๋งŽ์•˜๋‹ค. ๊ฑฐ์˜ ๋‚ด๊ฐ€ ๊ตฌ์„ฑํ•˜๊ณ  ์‚ฌ๋žŒ์„ ๋ชจ์œผ๊ธด ํ–ˆ๋‹ค.

  • MySQL

  • Python

  • Coursera ML

  • Computer Vision

  • Kaggle

  • Statistics

  • Linear Algebra

์ฐธ ๋งŽ์ด๋„ ํ–ˆ๋‹ค. ์•„์‰ฌ์šด ์  ์ด๊ฑธ ๊นƒํ—ˆ๋ธŒ์— ๊ธฐ๋กํ•˜๋ฉด์„œ ์ง„ํ–‰ํ–ˆ๋‹ค๋ฉด ๋”์šฑ ์ข‹์•˜์„ ๊ฒƒ ๊ฐ™๋‹ค. ๊ทธ๋Ÿฐ์ ์ด ์•„์‰ฝ๋„ค.

์ง€๊ธˆ์€ ๋‹ค์‹œ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์Šคํ„ฐ๋””๋ฅผ ๊ณง ์‹œ์ž‘ํ•˜๋Š”๋ฐ, ์ด๋ฒˆ์—๋Š” ๊นƒํ—ˆ๋ธŒ๋กœ ๊ด€๋ฆฌํ•˜๋ฉด์„œ ์ง„ํ–‰ํ•˜๋ ค๊ณ  ํ•œ๋‹ค.

  • Docker

  • Kubernetes

  • Django

  • DeepLearning Paper

7. SSAFY

์‹ธํ”ผ์— ์ง€์›ํ–ˆ๊ณ  ๋ถ™์—ˆ๋‹ค. ์‚ผ์„ฑ์ด๋ผ๋Š” ๋Œ€๊ธฐ์—… ๋ธŒ๋žœ๋“œ๊ฐ€ ์žˆ๋‹ค๋ณด๋‹ˆ ๊ฐ€์กฑ๋“ค๋„ ๊ฐ™์ด ๊ธฐ๋ปํ•ด์ฃผ๋Š” ๊ฒƒ ๊ฐ™๋‹ค. ๊ธฐ๋ถ„์ด ๋งŽ์ด ์ข‹๋„ค. ์‹œ์ž‘์€ ๋‹ค์Œ์ฃผ๋ถ€ํ„ฐ ํ•˜๋Š”๋ฐ ์ปค๋ฆฌํ˜๋Ÿผ์ด ์›Œ๋‚™ ๋‚ด๊ฐ€ ์›ํ•˜๋Š” ๋ถ„์•ผ์™€ ๋ฉ€๋‹ค๋ณด๋‹ˆ ๋ช‡ ๋‹ฌ ํ•˜๊ณ  ๊ทธ๋งŒ๋‘์ง€ ์•Š์„๊นŒ ํ•˜๋Š” ๊ฐ€๋Šฅ์„ฑ๋„ ์—ด์–ด๋‘๊ณ  ์žˆ๋‹ค.

SSAFY๋„ ์ข‹์ง€๋งŒ, AI Boostcamp๊ฐ€ ๋ฌด์กฐ๊ฑด ๋์œผ๋ฉด ์ข‹๊ฒ ๋‹ค. ๋‚ด์ผ ์ฝ”ํ…Œ ํŒŒ์ดํŒ….

๊ฐœ๋ฐœ ์™ธ

ํƒ๊ตฌ

๊พธ์ค€ํžˆ ํƒ๊ตฌ๋ฅผ ์ณ์™”์ง€๋งŒ, ์—ญ์‹œ ๋ ˆ์Šจ์„ ์•ˆ๋ฐ›์œผ๋‹ˆ๊นŒ ์‹ค๋ ฅ์ด ๊ณ„๋‹จํ˜• ์ƒ์Šน์ด๊ธฐ๋„ ํ•˜๊ณ . ๋™์•„๋ฆฌ๋‚˜ ๋ชจ์ž„๊ฐ™์€ ์†Œ์†๊ฐ์ด ์—†๋‹ค๋ณด๋‹ˆ ๋น„์˜ค๋ฉด ์•ˆ๊ฐ€๊ณ  ํ”ผ๊ณคํ•˜๋ฉด ์•ˆ๊ฐ€๊ณ  ์ฝ”๋”ฉ์ด ์žฌ๋ฐŒ์œผ๋ฉด ์•ˆ๊ฐ€๊ณ  ๊ฒŒ์ž„ํ•˜๋ฉด ์•ˆ๊ฐ€๊ณ  ํ•˜๋Š” ์ผ์ด ๋งŽ์•„์กŒ๋‹ค. ๊ทธ๋ž˜๋„ ์ฃผ 1ํšŒ๋Š” ๊พธ์ค€ํžˆ ์นœ ๊ฒƒ ๊ฐ™์€๋ฐ ์—ฐ์Šต๋Ÿ‰ ๋ถ€์กฑ์ธ์ง€ ์ •์ฒด๋œ ์‹ค๋ ฅ.

7์›” ๋ถ€ํ„ฐ๋Š” ์œ ๋ช…ํ™˜ ํƒ๊ตฌํด๋Ÿฝ (Famous ์•„๋‹˜)์—์„œ ๋ ˆ์Šจ์„ ๋ฐ›๊ธฐ๋กœ ํ–ˆ๋‹ค. ์ง€์—ญ 1๋ถ€ ๋ผ๋Š” ๋ฒ„ํ‚ท๋ฆฌ์ŠคํŠธ๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•œ ๋ณธ๊ฒฉ์ ์ธ ์ฒซ๊ฑธ์Œ์ด ์‹œ์ž‘๋œ๋‹ค.

๊ฐ€์ƒํ™”ํ

ํ€€ํŠธ ํˆฌ์ž์— ๊ด€ํ•œ ์ฑ…์„ ์ฝ๊ณ  ํŒŒ์ด์ฌ๊ณผ ์ ‘๋ชฉํ•˜๋‹ค๋ณด๋‹ˆ ๊ต‰์žฅํžˆ ์žฌ๋ฏธ์žˆ์–ด์กŒ๋‹ค. ์•„๋‹ˆ, ์žฌ๋ฏธ์žˆ๋˜ ๋•Œ๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ง€๊ธˆ์€ ์ข€ ํฅ๋ฏธ๊ฐ€ ์‹์—ˆ๋‹ค. ๊ทธ๋ž˜๋„ ๊ฐ€์ƒํ™”ํ๋กœ ๋ˆ์„ ๋ฒŒ๊ฒ ๋‹ค๋Š” ๋ชฉํ‘œ๊ฐ€ ์žˆ๋‹ค๋ณด๋‹ˆ, ์กฐ๋งŒ๊ฐ„ ๋‹ค์‹œ ์‹œ์ž‘ํ•ด๋ณผ ์˜ˆ์ •.

ํ€€ํŠธ ํˆฌ์ž๋ฅผ ๋ฐ˜๋Œ€ํ•˜๋Š” ์‚ฌ๋žŒ๋“ค์ด ์ถ”์ฒœํ•œ ์ฑ… '์ฐจํŠธ์˜ ๊ธฐ์ˆ '. ๊ต‰์žฅํžˆ ๋‘๊บผ์šด๋ฐ, ์ด๊ฒƒ๋„ ๋งˆ์ € ์ฝ๊ณ  ํ”„๋กœ๊ทธ๋ž˜๋ฐ ํ•ด๋ณด๋ ค๊ณ  ํ•œ๋‹ค.

  1. ํ€€ํŠธ ํˆฌ์ž ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๋งˆ๋ฌด๋ฆฌ

  2. '์ฐจํŠธ์˜ ๊ธฐ์ˆ ' ์ฝ๊ณ  ์ •๋ฆฌ

  3. '์ฐจํŠธ์˜ ๊ธฐ์ˆ ' ์ฝ”๋“œํ™”

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