| Day | Topic |
|---|---|
| Day1 | Vector & Matrix |
| Day2 | Gradient descent |
| Day3 | 확률론 - probability theory |
| Day4 | 통계학 - statistics |
| Day5 | CNN, RNN preview |
| Day | Topic |
|---|---|
| Day1 | DeepLearning Basic |
| Day2 | Optimization |
| Day3 | CNN |
| Day4 | RNN - Transformer |
| Day5 | Generative model |
| Day | Topic |
|---|---|
| Day1 | Introduction to PyTorch |
| Day2 | AutoGrad & optimizer |
| Day3 | Pre-trained model & monitoring tools |
| Day4 | Development details |
| Day | Topic |
|---|---|
| Day1 | EDA |
| Day2 | Dataset |
| Day3 | Model |
| Day4 | Train & inference |
| Day5 | Ensemble |
| Day | Topic |
|---|---|
| Day1 | Intro to NLP |
| Day2 | Basic RNN |
| Day3 | Sequence to Sequence |
| Day4 | Beam Search & BLEU Score |
| Day | Topic |
|---|---|
| Day1 | Transformer (1) |
| Day2 | Transforemr (2) |
| Day3 | Pretrained Language Model (1) |
| Day4 | Pretrained Language Model (2) |
| Day | Topic |
|---|---|
| Day1-1, Day1-2 | NLP review |
| Day2 | BERT |
| Day3 | single sentence classification - BERT |
| Day4 | two sentence classification -BERT |
| Day5 | Token classificaion - BERT |
| Day | Topic |
|---|---|
| Day1 | Generate model - GPT |
| Day3 | Latest NLP Tech |
| Day | Topic |
|---|---|
| Day1 | MRC intro & Python Basics |
| Day2 | Extraction & Generation based MRC |
| Day3 | Passage Retrieval - Sparse Embedding |
| Day3-2 | Passage Retrieval - Dense Embedding |
| Day4 | Passage Retrieval - Scaling up |
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