Gated Recurrent Units(GRUs) Table Of Contents: Disadvantages Of LSTM Networks. What Is GRU? Why We Need GRU Neural Network? (1) Disadvantages Of LSTM Networks. Computational Complexity: LSTM networks are more computationally complex compared to simpler architectures like feedforward neural networks or basic RNNs. This complexity arises due to the presence of multiple gates, memory cells, and additional parameters. As a result, training and inference can be more computationally expensive and time-consuming, especially for large-scale models and datasets. Memory Requirement: LSTM networks require more memory to store the additional parameters and memory cells. This can pose challenges when working with limited

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