RNN also has the problem of gradient

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disappearance and gradient explosion. It limits its long -term memory L and door control cycle unit RU that is optimized when dealing with long sequences. 1. Basic principles, when processing sequence data, we usually want to consider the dependency between elements in the sequence. For example, the meaning of a word when dealing with natural language may depend on the words in front of it. Traditional neural networks cannot handle this dependence


because they he activation function. This process will be updated to the hidden state and output on the entire sequence. In this way, the output of each time step will take into account the current input and all past inputs to capture the dependencies in the sequence. What kind of person is suitable for the Rich People Phone Number List product manager to become an excellent product manager who understands business and products is two very important standards. The speed of the iteration and promotion of the end track is also very slow. This generates a lot of job opportunities to view the details of the input of a natural language sentence. When dealing with each word,





RNN will not only take into account the word, but also consider all the words in front of the word. In this way, RNN can understand the semantics of the sentence to perform tasks such as emotional analysis or machine translation. Suppose we are dealing with an emotional analysis task. Our goal is whether the emotional emotion of the comment is positive or negative based on the text of the movie