[hongong_machine_study1] 3, 4주차 활동 인증 / 5, 6주차 활동 인증#21
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Nov 9, 2025
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- 개인별 공부내용 요약정리 md파일 업로드
- 개인별 실습 ipynb 파일 업로드
Added content on logistic regression, including data preparation, probability prediction, and implementation of logistic regression for binary and multi-class classification.
Added sections on stochastic gradient descent and loss function.
Add sections on loss functions and SGDClassifier with examples.
Added explanations for five optimizers: Momentum, Adagrad, RMSProp, Adam, and AdamW, including their concepts and the problems they address.
Add chapter on decision trees and logistic regression for wine classification, including code examples and visualizations.
Add detailed explanations and code examples for cross-validation, grid search, and random search in model evaluation and hyperparameter tuning.
Added sections on ensemble learning, including Random Forest, Extra Trees, Gradient Boosting, and Histogram-based Gradient Boosting. Included Python code examples for cross-validation and feature importance.
tttyoung
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Added a detailed markdown document on IMDB review classification using recurrent neural networks, including data preprocessing, model creation, and training.
Added detailed explanations and code examples for LSTM and GRU architectures, including training procedures and dropout techniques.
Added a summary of deep learning models including RNN, LSTM, Attention, BERT, and Transformer, highlighting their commonalities and differences.
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