Smpler than L, RU may be better than L

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training than L in certain tasks. The figure below is the principle of L. The specific principle of the diagram of L is not here to detail students who are interested can inquire themselves. The difference between L and RU L and Ru are the variants of RNN. Update doors and reset doors. The number L in the state maintains two states. One is the state of the cell. Ru has only one hidden state. The complexity is more complicated than RU because


L has more doors and states. This may make L need more computing resources during training. Ru on some tasks and datasets may be more effective and better than L training. Memory ability theoretically, the design principle based on L should be able to better deal with long -term Rich People Phone Number List dependence. Fourth, the application scenario RNN has been widely used in processing of various sequence data due to its unique circulating structure, which has a natural advantage in processing sequence data. Here are some common application scenarios voice recognition for modeling audio signals to achieve voice recognition. Language model is used to



predict the next word to achieve language models. This is very useful in mission such as machine translation text. Machine translation is used to encode the source language sequence and decoding target language sequence to achieve machine translation. Emotional analysis is used to analyze the emotions such as positive or negative. Video processing is used to process video sequences such as acti