পদ্ধতির তুলনা করুন

	ব্যাখ্যাযোগ্য GRU	লং শর্ট-টার্ম মেমরি (এলএসটিএম)
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2014 (GRU); 2016–2017 (XAI integration)	1997
প্রবর্তক	Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME)	Hochreiter, S. & Schmidhuber, J.
ধরন	Recurrent neural network with post-hoc or attention-based interpretability	Recurrent neural network with gated memory cells
মৌলিক উৎস	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
অপর নাম	XAI-GRU, Interpretable GRU, GRU with explainability, Transparent GRU	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
সম্পর্কিত	5	4
সারসংক্ষেপ	Explainable GRU pairs the Gated Recurrent Unit, a compact and efficient recurrent architecture, with explainability techniques such as SHAP, LIME, or attention weighting to reveal which time steps and features drove each prediction. It brings interpretability to sequential modelling without sacrificing the GRU's ability to capture temporal dependencies.	Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step.