Introduction to AutoML and hyperparameter optimization using Bayesian Optimization with Gaussian Process Regression.

Week 11 retrospective of Naver Boostcamp AI Tech covering the Relation Extraction competition, model experiments, and leaderboard submissions.

Extraction-based machine reading comprehension: span prediction with BERT, preprocessing, fine-tuning, and post-processing.

Week 10 of Naver Boostcamp AI Tech with a retrospective on the Relation Extraction competition.

Week 9 of Naver Boostcamp AI Tech covering NLP competition setup, model experimentation, and leaderboard submissions.

Summary of language model benchmarks from GLUE to multilingual evaluations, covering the evolution from RNN to ELMo, BERT, and beyond.

Discussion of AI bias, fairness, and privacy issues, including the COMPAS case study and COVID contact tracing privacy analysis.

What it means to be a full-stack ML engineer: roles, ML product lifecycle, team structure, and trade-offs.

Overview of copyright and licensing considerations for AI projects, covering CCL, fair use, and the legal gray areas of AI-generated content.

Week 8 retrospective of Naver Boostcamp AI Tech covering NLP lectures, MLOps tooling research, and validation set distribution discussions.

Practical tips for Kaggle competitions covering ranking systems, competition types, validation strategies, ensemble methods, and hardware recommendations.

End-to-end process of deploying AI models as services, from requirements analysis and dataset construction to modeling, testing, and team organization.

Week 7 retrospective of Naver Boostcamp AI Tech covering Transformer and BERT studies, mentoring Q&A on attention mechanisms and positional encoding.

Week 6 study summary of Naver Boostcamp AI Tech covering NLP lectures, bucketing for data batching, and peer session discussions.

Retrospective on a mask/age/gender image classification competition: model selection, augmentation, CutMix, and lessons learned.

Week 4 retrospective of Naver Boostcamp AI Tech covering image classification competition, CNN model comparisons, and CutMix augmentation.

TIL notes on wandb logging, fine-tuning pre-trained models, early stopping, and ensemble learning during an image classification competition.

Day 2 competition log covering ensemble strategies (bagging vs. boosting) and CNN model selection for image classification.

Training plan for a mask classification competition: per-feature models with ensemble learning, plus Jupyter vs. CLI workflow notes.

Week 3 of Naver Boostcamp AI Tech covering PyTorch custom models, datasets, dataloaders, and data visualization.

Introduction to generative models: probability distributions, independence assumptions, chain rule, and auto-regressive models.

Week 2 of Naver Boostcamp AI Tech covering deep learning basics, data visualization, and sample variance with degrees of freedom.

Discussion on how the relationship between model parameter count and generalization performance shifted with scaling law findings.

Week 1 retrospective of Naver Boostcamp AI Tech covering AI math fundamentals, Python basics, and gradient descent implementation.