그래서 준비했습니다. RNN과 LSTM 이해를 위한 완벽가이드! 금쪽같은 블로그인 Colah의 블로그 내용을 기반으로 RNN과 LSTM의 내부 메커니즘과 수식을 설명해보았습니다. 그럼 즐겁게 들어주세요~
[Link] Colah의 블로그, http://colah.github.io/posts/2015-08-Understanding-LSTMs/
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LSTM stands for Long Short-Term Memory, a type of recurrent neural network (RNN) designed to learn patterns in sequential data while overcoming the limitations of traditional RNNs. It uses specialized memory cells and gates to retain important information over long periods, making it highly effective for tasks such as language translation, speech recognition, text prediction, sentiment analysis, and time-series forecasting. Because of its ability to capture long-term dependencies, LSTM remains one of the most widely used deep learning models for sequence-based applications.
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