This paper evaluates the performance of different types of recurrent units in recurrent neural networks (RNNs), focusing on more sophisticated units that implement a gating mechanism, such as the long short-term memory (LSTM) unit and the gated recurrent unit (GRU). The evaluation is conducted on two tasks: polyphonic music modeling and speech signal modeling. The results show that these advanced recurrent units outperform traditional units like tanh units. Specifically, the GRU unit is found to be comparable to the LSTM unit. The study also highlights the advantages of gating units, including faster convergence and better generalization, but concludes that the choice between LSTM and GRU may depend on the specific dataset and task. The paper provides a detailed comparison of the LSTM and GRU units, their architectures, and their performance on various datasets, emphasizing the importance of gated units in handling long-term dependencies and improving the overall performance of RNNs.This paper evaluates the performance of different types of recurrent units in recurrent neural networks (RNNs), focusing on more sophisticated units that implement a gating mechanism, such as the long short-term memory (LSTM) unit and the gated recurrent unit (GRU). The evaluation is conducted on two tasks: polyphonic music modeling and speech signal modeling. The results show that these advanced recurrent units outperform traditional units like tanh units. Specifically, the GRU unit is found to be comparable to the LSTM unit. The study also highlights the advantages of gating units, including faster convergence and better generalization, but concludes that the choice between LSTM and GRU may depend on the specific dataset and task. The paper provides a detailed comparison of the LSTM and GRU units, their architectures, and their performance on various datasets, emphasizing the importance of gated units in handling long-term dependencies and improving the overall performance of RNNs.