פיתוח בינה מלאכותית : RNN
RNN טובה לנתח אירוע של סדרות של מספרים
- לבצע ניבוי לאיבר הבא בסדרות של מספרים \ נתונים
Time Series Prediction: Forecasting stock prices, weather prediction, or any other time-dependent data
- לבצע איתור של חרגיע בסדרות שחזורות על עצמם
Anomaly Detection: Detecting unusual patterns in time-series data.
שימושים טובים
Sequence Classification: Classifying data sequences, such as sentiment analysis on text or identifying activities from sensor data
Relevance Today
While RNNs are still relevant, they have been largely supplanted by more advanced architectures like Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and Transformers. These newer architectures address some of the limitations of vanilla RNNs, such as difficulty in learning long-range dependencies and vanishing gradient problems.
Transformers, in particular, have become the state-of-the-art for many NLP tasks due to their ability to handle long-range dependencies more efficiently and their parallel processing capabilities. However, RNNs and their variants (LSTMs and GRUs) are still useful in certain applications, especially where computational resources are limited or where the sequential nature of data is more straightforward to capture with these models.
RNN אם סידרת המספרים ארוכה – RNN התקשה לעבד אותה ולכן יהיה עדיף מודל אחר
LSTM GRU
