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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. : Retrospective
  2. 21Y

7M1W

210710

2021๋…„ 7์›” ์ฒซ์งธ์ฃผ ํšŒ๊ณ 

7์›” 4์ผ ~ 7์›” 10์ผ๊ฐ„์˜ ํšŒ๊ณ ์ด๋‹ค.

  • ์‹ค์ œ ํ‘œ์ค€ ๊ทœ์•ฝ์€ ๋‘˜์งธ์ฃผ์ด๋‹ค.

TIL

๊ณ„ํš

์ฒซ์งธ ์ฃผ 4-10
- hfp 11, 11_3/4, 12
- ๋ฐ‘์‹œ๋”ฅ 7, 8
- SSAFY ๊ณผ์ œ
- 1์ผ 1A
- LA 13-42
- CNN 8-13
- Django 0-7
- ๋„์ปค 0-8

- ๊ธฐ์ƒ, ์ทจ์นจ ์ฒดํฌ๋ฆฌ์ŠคํŠธ
- 6์‹œ๊ฐ„ ์ด์ƒ ์ฒดํฌ๋ฆฌ์ŠคํŠธ
- ๋งค์ฒด ์‹œ์ฒญ ์ฒดํฌ๋ฆฌ์ŠคํŠธ

์„ฑ์ทจ

์ฒซ์งธ ์ฃผ 4-10
- hfp 
- ๋ฐ‘์‹œ๋”ฅ 
- SSAFY ๊ณผ์ œ
- 1์ผ 1A
- LA
- CNN
- Django 0-3
- ๋„์ปค

- ๊ธฐ์ƒ, ์ทจ์นจ ์ฒดํฌ๋ฆฌ์ŠคํŠธ
- 6์‹œ๊ฐ„ ์ด์ƒ ์ฒดํฌ๋ฆฌ์ŠคํŠธ
- ๋งค์ฒด ์‹œ์ฒญ ์ฒดํฌ๋ฆฌ์ŠคํŠธ

ํ”ผ๋“œ๋ฐฑ

์ผ๋‹จ, ์ €๋ฒˆ์ฃผ ํ”ผ๋“œ๋ฐฑ ๋ณด๊ณ . ์ทจ์นจ๊ณผ ๊ธฐ์ƒ์ด ๋„ˆ๋ฌด ๋Šฆ์–ด์„œ ๋ฒŒ๊ธˆ ์ œ๋„๋ฅผ ์ž์ฒด์ ์œผ๋กœ ๋„์ž…ํ•˜๋ฉด์„œ ๊ธฐ์ƒ๊ณผ ์ทจ์นจ์‹œ๊ฐ„์„ ์ง€ํ‚ค๊ธฐ๋กœ ํ–ˆ๋‹ค. ๋˜ ๊ณต๋ถ€ํ•˜๋ฉด์„œ ์œ ํŠœ๋ธŒ๋ฅผ ๋ณด๊ฑฐ๋‚˜ ํœด๋Œ€ํฐ์„ ๋งŽ์ดํ•ด์„œ ์ด๊ฒƒ๋„ ์ฒดํฌํ•˜๊ธฐ๋กœ ํ–ˆ๊ณ  ํ•˜๋ฃจ ๊ณต๋ถ€์‹œ๊ฐ„์„ ์žฌ๊ธฐ๋กœ ํ–ˆ๋‹ค.

์ฒซ๋‚ ๋ถ€ํ„ฐ ๊ธฐ์ƒ์„ ๋ชปํ•ด์„œ ์นœ๊ตฌ์—๊ฒŒ 5์ฒœ์›์„ ๋ณด๋ƒˆ๋‹ค. ์ดํ›„์—๋Š” 7์‹œ์— ์ผ์–ด๋‚˜์ง€๋Š” ๋ชปํ–ˆ๊ณ  9์‹œ์— ์‹ธํ”ผ๊ฐ€ ์‹œ์ž‘๋˜๊ธฐ ๋•Œ๋ฌธ์— 8์‹œ 30๋ถ„์— ์ผ์–ด๋‚ฌ๋‹ค. ๋ฌผ๋ก  ๋Šฆ๊ฒŒ ์ผ์–ด๋‚˜๊ฒŒ ๋œ ์ด์œ ๋Š” ์ „๋‚  ์ทจ์นจ์ด 12์‹œ์— ์ด๋ฃจ์ง€ ๋ชปํ•˜๊ณ  ์ƒˆ๋ฒฝ์— ์ทจ์นจํ–ˆ๊ธฐ ๋•Œ๋ฌธ. ์‚ฌ์‹ค ํ•‘๊ณ„์ง€๋งŒ, ๋‚ด๊ฐ€ ํ•˜๊ณ ์‹ถ์€ ๊ณต๋ถ€๋ฅผ ๋ชปํ•˜๊ณ , ์‹ธํ”ผ๊ฐ€ ์ƒ๊ฐ๋ณด๋‹ค ์žฌ๋ฏธ์žˆ์ง€ ์•Š์•„์„œ ๋‹ค์Œ๋‚  ์ผ์ฐ ์ผ์–ด๋‚˜์„œ ๊ณต๋ถ€ํ•ด์•ผ ํ•œ๋‹ค๋Š” ์˜์ง€๊ฐ€ ๋‚ฎ์•„์ง€๊ธด ํ–ˆ๋‹ค. ๊ทธ๋ž˜๋„ ์‹ธํ”ผ๋ฅผ ํ•˜๋‹ค๋ณด๋‹ˆ ๋ฐ˜ ๊ฐ•์ œ๋กœ ๊ณต๋ถ€์‹œ๊ฐ„์ด ๋งŽ์•„์ง€๊ณ  ๋”ด์ง“๋„ ์ž˜ ์•ˆํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค.

์ด๋ฒˆ ํ•œ์ฃผ๋Š” ๊ทธ๋ž˜๋„ ๋Œ€์ฒด๋กœ ์ €๋ฒˆ์ฃผ๋ณด๋‹ค ๋งŽ์ด ๋‚˜์•„์กŒ๋‹ค. ๊ฒŒ์ž„๋„ ๊ฑฐ์˜ ์•ˆํ–ˆ๊ณ . ๊ธฐ์ƒ๋„ ๋ชฉํ‘œ 7์‹œ๋Š” ๋ชป์ง€์ผœ๋„ 9์‹œ ์ด์ „์—๋Š” ๊พธ์ค€ํžˆ ํ–ˆ๋‹ค. ์ทจ์นจ์„ ์ข€ ๋Šฆ๊ฒŒํ•œ ๋А๋‚Œ์€ ์žˆ๋‹ค. ๋”ด์ง“์€ ๊ฐ•์ œ์ง€๋งŒ ์•ˆํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค.

๋‹ค์Œ์ฃผ๋Š” ๊ธฐ์ƒ, ๊ณต๋ถ€์‹œ๊ฐ„ ์ •๋„๋งŒ ์ฒดํฌํ•˜๋ ค๊ณ  ํ•œ๋‹ค.

์ง„ํ–‰๋„

  • hfp : 0%

  • ๋ฐ‘์‹œ๋”ฅ : 0%

  • SSAFY ๊ณผ์ œ : 100%

  • 1์ผ 1A : 100%

  • LA : 0%

  • CNN : 0%

  • Django : 33%

  • ๋„์ปค : 0%

ํ‰๊ท  : 30%

๋ฐ˜์„ฑ

  • ์ง€๋‚œ์ฃผ(31%)์™€ ๋น„์Šทํ•œ ์ˆ˜์น˜์ง€๋งŒ ํ• ๋ง์€ ๋งŽ์€ ์ฃผ. ์‹ธํ”ผ ํ”„๋กœ๊ทธ๋žจ์„ ์ง„ํ–‰ํ•˜๋А๋ผ ๋‚˜์˜ ๊ณต๋ถ€๊ณ„ํš์„ ๊ฑฐ์˜ ์ดํ–‰ํ•˜์ง€ ๋ชปํ–ˆ๋‹ค. ์ˆ˜์š”์ผ์ด ์‹ธํ”ผ ์ฒซ ์‹œ์ž‘์ด์—ˆ๋Š”๋ฐ, ์›”ํ™”๋„ ๊ฑฐ์˜ ์‹ธํ”ผ ์‚ฌ์ „๊ต์œก์„ ๋“ฃ๋А๋ผ ์žฅ๊ณ ์™€ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์ •๋„๋งŒ ๊ฒจ์šฐ ํ–ˆ๋‹ค.

  • ์ด๋ฒˆ์ฃผ๋Š” ์‹ธํ”ผ ๊ต์œก์„ ๊ทธ๋งŒ๋‘๊ณ  ๋ถ€์บ  AI๋ฅผ ์ค€๋น„ํ•˜๋Š” ๊ธฐ๊ฐ„์ด๋ผ ์ข€ ๋” ์—ฌ์œ ๋กœ์šธ ๊ฒƒ ๊ฐ™์•„์„œ ๊ทธ๋™์•ˆ ๋ชปํ•œ ๊ณต๋ถ€๋ฅผ ์™„๋ฒฝํžˆ ๋๋‚ด๊ณ  ์‹ถ๋‹ค. HFP, ๋ฐ‘์‹œ๋”ฅ, RNN, ์‹œ๊ฐํ™” ์–ผ๋งˆ๋‚˜ ์˜ค๋ž˜ ๊ฐ€์ง€๊ณ  ์žˆ๋Š”๊ฑฐ๋ƒ!! ์–ผ๋ฅธ ๋นจ๋ฆฌ ๋๋‚ด๋ฒ„๋ฆฌ์ž!!

  • ๋„์ปค ์Šคํ„ฐ๋””์™€ ์žฅ๊ณ  ์Šคํ„ฐ๋””๋ฅผ ๊ฑฐ์˜ ๊ณต๋ถ€ ๋ชปํ–ˆ๋‹ค. ๋ฒŒ๊ธˆ๋„ ๋ƒˆ๋‹ค. ๋‹ค์Œ์ฃผ๋Š” ์ด๋ฒˆ์ฃผ ๋ชซ๊นŒ์ง€ ํ•ด์•ผํ•œ๋‹ค ์ข€๋” ํ™”์ดํŒ…!

  • CNN ๊ฐ•์˜๋„ ๋„ˆ๋ฌด ์˜ค๋ž˜์žก๊ณ  ์žˆ๋‹ค. ์–ผ๋ฅธ ๋‹ฌ๋ ค๋ณด์ž!

์นญ์ฐฌ

  • ์ด๋ฒˆ์ฃผ๋„ 1์ผ 1์ปค๋ฐ‹ํ•˜๋А๋ผ ๊ณ ์ƒํ–ˆ๋‹ค. ์‚ฌ์‹ค ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ‘ธ๋Š” ๊ฒŒ ๊ฑฐ์˜ ํƒœ๋ฐ˜์ด๋ผ ์ด๊ฑฐ ์•ˆํ’€๋ฉด ๋ญ๋กœ ์ปค๋ฐ‹ํ–ˆ์„๊นŒ ์‹ถ๊ธฐ๋„ ํ•˜์ง€๋งŒ ๊ทธ๋ž˜๋‘ ํ‘ธ๋Š”๋ฐ ํ•œ์‹œ๊ฐ„์€ ๊ฑธ๋ฆฌ๊ธฐ ๋•Œ๋ฌธ์— ์–ด์ง€๊ฐ„ํ•œ ๊ฐ•์˜ ์–‘ ์ •๋„๋Š” ๋˜์ง€ ์•Š์„๊นŒ?

  • ์‹ธํ”ผ ๊ต์œก๊ณผ์ • ์žฌ๋ฏธ์—†์—ˆ์ง€๋งŒ ์ ๊ทน์ ์œผ๋กœ ์ฐธ์—ฌํ•˜๋А๋ผ ๊ณ ์ƒํ–ˆ์–ด!! ๊ทธ๋ž˜๋„ ์Šคํƒ€ํŠธ์บ ํ”„ ์ฒซ์ฃผ์˜ ์กฐ1๋“ฑ์„ ํ•˜๊ฒŒ ๋˜์—ˆ๋„ค! ์ข‹์€ ์กฐ์›๋„ ๋งŒ๋‚˜์„œ ๋” ์ž˜๋๋˜ ๋“ฏ! ์ด์ œ ๋‹ค๋“ค ๋น ์ด๋น ์ด์ง€๋งŒ...! ์—ด์‹ฌํžˆ ์ฐธ์—ฌํ•˜๋ ค๊ณ  ํ•œ ๋‚˜์—๊ฒŒ ์นญ์ฐฌ!

๊ทธ ์™ธ

1. ๋ถ€์ŠคํŠธ์บ ํ”„ AI

ํ•ฉ๊ฒฉ!!!!! 7์›”16์ผ ๊ธˆ์š”์ผ๋ถ€ํ„ฐ ์‹œ์ž‘์ด๋‹ค! ์ด๋ฏธ ์ฐธ์—ฌํ•˜๊ฒ ๋‹ค๊ณ  ๋‹ต์‹ ๋„ ๋ณด๋ƒˆ๊ณ . ์‹ธํ”ผ๋Š” ์•„์‰ฝ์ง€๋งŒ ์—ฌ๊ธฐ๊นŒ์ง€์ธ๊ฑธ๋กœ! 100๋งŒ์› ๋ชป๋ฐ›๋Š”๊ฑด ๋งŽ์ด ์•„์‰ฝ๋„ค ํžˆํžˆ

2. ์Šคํ„ฐ๋””

์šฐ๋ฆฌ ์žฅ๊ณ , ๋„์ปค, ๋…ผ๋ฌธ๋ถ„์„ ์Šคํ„ฐ๋”” ์ง„ํ–‰๋ฅ ์ด ๊ฑฐ์˜ ์—†๋‹ค. ๋‹ค ๋‚ด๊ฐ€ ๊ทธ๋ฆฌ๊ณ  ๋‚˜๋งŒ ์—ด์‹ฌํžˆ ํ•˜๋ฉด ๋‹ค๋“ค ์ž˜ ๋”ฐ๋ผ์˜ฌํ…๋ฐ. ๋‚˜๋„ ๋’ค์ณ์ง€๋А๋ผ ๋ˆ„๊ตฌ๋ฅผ ๋‹ค๋…์ผ ์—ฌ์œ ์™€ ์ž…์žฅ์ด ์•ˆ๋˜๋„ค. ์ด๋ฒˆ์ฃผ๋Š” ํ™”์ดํŒ…ํ•˜์ž

3. ์‹ธํ”ผ

์ด๋ฒˆ์ฃผ๊ฐ€ ์‹ธํ”ผ ๋งˆ์ง€๋ง‰! ๋ฌผ๋ก  ๋ถ€์บ  OT ์‹œ์ž‘ํ•˜๊ธฐ ์ „๊นŒ์ง€ ๋“ฃ๊ณ  ์‹ถ๊ธด ํ•œ๋ฐ, ์ง€๊ธˆ ๋‹น์žฅ ๊ณต๋ถ€ํ•  ๊ฒŒ ๋„ˆ๋ฌด๋งŽ์•„์„œ ๊ทธ๋ ‡๊ฒŒ๋Š” ๋ชปํ•  ๊ฒƒ ๊ฐ™๋‹ค. ๊ฒŒ๋‹ค๊ฐ€, ์Šคํƒ€ํŠธ์บ ํ”„๊ฐ€ 2์ฃผ๊ฐ„ 8์‹œ๊ฐ„์งœ๋ฆฌ ๊ต์–‘๋“ฃ๋Š” ๋А๋‚Œ์ด๋ผ ์—„์ฒญ ์ง„์ด ๋น ์ง„๋‹ค. ๋ง‰ ์ „๊ณต ๊ด€๋ จ๋œ ํ”„๋กœ์ ํŠธ๋„ ์•„๋‹ˆ๋ผ์„œ ์™„์ „ ๊ต์–‘ ์กฐ๋ณ„ ๋ชจ์ž„ ํ•˜๋Š” ๋А๋‚Œ..

4. ์ฝ”๋กœ๋‚˜

์ฝ”๋กœ๋‚˜๊ฐ€ ๋„ˆ๋ฌด ์‹ฌ๊ฐ.. 1200๋ช… ... ๋งค์ผ ๊ธฐ๋ก ๊ฐฑ์‹  ์ค‘..

5. ๊ตฌ๊ธ€ ์„œ์น˜ ์ฝ˜์†”

๋‚ด ๊นƒ๋ถ ๋ธ”๋กœ๊ทธ๊ฐ€ ๊ตฌ๊ธ€์— ๊ฒ€์ƒ‰์ด ์•ˆ๋œ๋‹ค. ์ง„์งœ ์•„์˜ˆ ์•ˆ๋œ๋‹ค. ์ธ๋ฑ์‹ฑ ์šฐ์„ ์ˆœ์œ„๊ฐ€ ๋‚ฎ์€๊ฒŒ ์•„๋‹ˆ๋ผ ์•„์˜ˆ ์—†๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋Š” ์„œ์น˜ ์ฝ˜์†”๊ณผ ์—ฐ๊ฒฐ๋˜๋ฉด ์–ด๋А ์ •๋„ ํ•ด๊ฒฐ๋œ๋‹ค๊ณ  ํ•˜๋Š”๋ฐ, ์„œ์น˜ ์ฝ˜์†”๊ณผ ์—ฐ๊ฒฐํ•˜๋ ค๋ฉด ๋˜ ๊ตฌ๊ธ€ ์• ๋„๋ฆฌํ‹ฑ์Šค๊ฐ€ ํ•„์š”ํ•ด์„œ ์ด๊ฒƒ์ €๊ฒƒ ๋‹ค ํ•˜๋А๋ผ ์‹œ๊ฐ„์„ ๋งŽ์ด ์ผ๋‹ค. ๊ทธ๋™์•ˆ ์“ด ์‹œ๊ฐ„๋งŒ 10์‹œ๊ฐ„ ๊ฐ€๊นŒ์ด ๋˜๋Š” ๊ฒƒ ๊ฐ™๋‹ค.

๊ฒฐ๋ก ์€, ๋ฏธํ•ด๊ฒฐ. ์•„๋ฌด๋ฆฌ Support์— ๋ฌธ์˜ํ•ด๋„ ์šฐ๋ฆฌ๊ฐ€ ์ ์–ด๋†“์€ gitbook ํŠœํ† ๋ฆฌ์–ผ ๋ด๋ผ ํ•˜๊ณ  ๋งํฌ๋งŒ ๋‚จ๊ฒจ์ค€๋‹ค. ์• ์ดˆ์— gitbook์ด html ์ˆ˜์ •์ด ์•ˆ๋ผ์„œ ์„œ์น˜์ฝ˜์†” ์—ฐ๊ฒฐํ•˜๊ธฐ๋„ ํž˜๋“ค๊ณ  ๊ทธ ์™ธ์— ์ถ”์  ์ฝ”๋“œ๋ฅผ ์‚ฝ์ž…ํ•˜๋Š” ๋ฐฉ๋ฒ•๋„ ์ž˜ ๋ชฐ๋ผ๊ฐ€์ง€๊ณ  ์–ด๋–ป๊ฒŒ ํ•  ์ˆ˜๊ฐ€ ์—†๋‹ค. ์žฅ๋ฌธ์˜ Question์„ ๋ณด๋ƒˆ๋”๋‹ˆ ์•„์˜ˆ ์ฝ์”น. ์•„๋‹ˆ ์„œ๋น„์Šค ์ •์‹ .. ์ด๋ž˜๋„ ๋˜๋Š”๊ฑด๊ฐ€. ๋‚˜๋„ ๋‚˜๋ฆ„ ๊ธธ๊ฒŒ ์“ด ๊ฒƒ ๊ฐ™์•„์„œ ๋ฏธ์•ˆํ•˜๋‹ค๊ณ ๋„ ์ค„๋งˆ๋‹ค ํ•˜๊ณ  ์Šคํฌ๋ฆฐ ์ƒท ์ฐ์€๊ฑฐ ์ดํ•ดํ•˜๊ธฐ ์–ด๋ ค์šธ๊นŒ๋ด ํ•˜๋‚˜ํ•˜๋‚˜ ๋ฒˆ์—ญ๋„ ๋‹ฌ์•„์คฌ๊ฑด๋งŒ.. ๊นƒ๋ถ์€ ์ง„์งœ ํ”ผ๋“œ๋ฐฑ์ด๋ž‘ ์—…๋ฐ์ดํŠธ๊ฐ€ ์“ฐ๋ ˆ๊ธฐ์ด๋‹ค. ์‹œ์ž‘์„ ๊นƒํ—ˆ๋ธŒ์•„์ด์˜ค๋กœ ํ–ˆ์–ด์•ผ ๋๋”ฐ ์ •๋ง.. ์•„๋‹ˆ๋ฉด ๋…ธ์…˜... ์ง€๊ธˆ์ด๋ผ๋„ ๊ฐˆ์•„ํƒ€๊ณ  ์‹ถ์€๋ฐ.. ํ•˜์•„.. ๋„ˆ๋ฌด ๊ณจ์น˜..

์•„๋‹ˆ ๊ทผ๋ฐ ์ฝ์”น์€ ์ง„์งœ ๋„ˆ๋ฌดํ•œ๊ฑฐ ์•„๋…€? ํ•ด๊ฒฐํ•˜์ง€ ๋ชปํ•œ๋‹ค๊ณ  ํ•˜๋Š”๊ฒƒ๋„ ํ™”๋‚  ๊ฒƒ ๊ฐ™์ง€๋งŒ..

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